Methods and devices for assessing risk of female infertility

ABSTRACT

The invention generally relates to methods and devices for assessing risk of female infertility. In certain aspects, methods of the invention involve obtaining a sample, conducting an assay on at least one infertility-associated biomarker, and assessing risk to the patient of developing early-onset decrease in fertility based upon results of the assay.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Non-Provisional Ser. No. 14/107,800, filed Dec. 16, 2013, which claims priority to U.S. Provisional Nos. 61/889,738, filed Oct. 11, 2013, and 61/737,693, filed Dec. 14, 2012. This application also claims priority to and the benefit of U.S. Provisional No. 61/932,226, filed Jan. 27, 2014. The aforementioned applications are incorporated by reference herein.

TECHNICAL FIELD

The invention generally relates to methods and devices for assessing risk of female infertility.

BACKGROUND

Approximately one in seven couples has difficulty conceiving. Infertility may be due to a single cause in either partner, or a combination of factors (e.g., genetic factors, diseases, or environmental factors) that may prevent a pregnancy from occurring or continuing. Every woman will become infertile in her lifetime due to menopause. On average, egg quality and number begins to decline precipitously at 35. However, some women experience this decline much earlier in life, while a number of women are fertile well into their 40's. Though, generally, advanced maternal age (35 and above) is associated with poorer fertility outcomes, there is no way of diagnosing egg quality issues in younger women or knowing when a particular woman will start to experience decline in her egg quality or reserve.

The elucidation of the genetic basis of female infertility disorders permits the development of powerful, rapid, and non-invasive diagnostic tools that will help clinicians direct patients to efficient and effective treatment options. Additionally, the discovery of the key genes underlying these disorders holds great promise for the identification of novel targets for drug development and therapeutics. Finally, a better understanding of the crucial molecular pathways underlying human fertility guides the next generation of targeted, non-hormonal contraceptives.

SUMMARY

The invention provides applications and methods for determining the identity of genetic loci biologically or statistically correlated with increased risk of susceptibility of an individual to infertility or early-onset decrease in fertility (premature menopause). In one aspect, the invention provides nucleic acid sequences that can be used to assess the presence or absence of particular nucleotides at polymorphic sites in an individual's RNA or genomic DNA that are associated with susceptibility to decreased fertility. In certain aspects, the invention provides methods for observing commonly occurring or rare genetic variants within a subset of genes of interest for human infertility and risk of premature menopause. In certain aspects, the invention provides methods for ranking the relative importance of individual genetic variants, genes, or genetic regions for allowing determination of infertility or premature menopause risk. In certain aspects, the invention provides a method for identifying a human subject as having an increased risk for infertility or premature menopause, including the following steps: 1) obtaining a sample from a patient; 2) conducting an assay on at least one infertility-associated biomarker; and 3) assessing risk to the patient of developing early-onset decrease in fertility.

As discussed below, an array of genetic information concerning the status of various infertility-related genetic regions is used in order to assess the risk of a subject having an increased susceptibility to reduced fertility, premature menopause, or infertility. The genetic information may include one or more polymorphisms in one or more infertility-related genetic regions, mutations in one or more of those genetic regions, or particular epigenetic signatures affecting the expression of those genetic regions. The molecular consequence of these genetic region mutations could be one or a combination of the following: alternative splicing, lowered or increased RNA expression, and/or alterations in protein expression. These alterations could also include a different protein product being produced, such as one with reduced or increased activity, or a protein that elicits an abnormal immunological reaction. All of this information is significant in terms of informing a patient of her susceptibility to infertility or reduced fertility relative to her age or other relevant phenotypes such as hormone levels or ovarian follicle count.

In addition to looking exclusively at genomic information, by combining genetic information (e.g., polymorphisms, mutations, etc.) with phenotypic and/or environmental data, methods of the invention provide an additional level of clinical clarity. For example, polymorphisms in genes discussed below may provide information about a disposition toward infertility or reduced fertility. However, in certain cases, the clinical outcome may not be determinative unless combined with certain phenotypic and/or environmental information. Thus, methods of the invention provide for a combination of genetic predispositional analyses in combination with phenotypic and environmental exposure data in order to assess the potential for infertility or reduced fertility relative to age. Thus, in certain cases, genetic predisposition may be sufficient to make a diagnosis, but in other cases, the clinical outcome may not be clear based upon genetic analysis alone and the combination of genetic and phenotypic or environmental data must be used in order to assess the likelihood of infertility or reduced fertility.

In addition to providing information to women related to the risk of infertility or reduced fertility if she chooses to try for a child at a particular age, methods of the invention may also be used by a physician for treatment purposes, e.g., allowing a physician to make vitamin/drug recommendations to help reduce or eliminate the risk to early-onset reduction in fertility. For example, data herein show that a mutation in the CBS gene affects infertility. This data may be used by a physician to generate a treatment plan that may help remediate the infertility risk in the woman. For example, the physician may advise the woman to take a high dose of folic acid or other vitamin supplements/drugs in order to improve fertility. Such a treatment plan may reduce or eliminate the infertility risk in the woman.

A biomarker generally refers to a molecule that acts as an indicator of a biological state. In certain embodiments, the biomarker is a genetic region. In particular embodiments, the genetic region is an infertility-related genetic region. Any assay known in the art may be used to analyze the genetic region. In certain embodiments, the assay includes sequencing at least a portion of the genetic region to determine presence or absence of a mutation that is associated with infertility. Mutations detected according to the invention may be any type of genetic mutation. Exemplary mutations include a single nucleotide polymorphism, a deletion, an insertion, an inversion, other rearrangements, a copy number variation, or a combination thereof. Any method of detecting genetic mutations is useful with methods of the invention, and numerous methods are known in the art. In certain embodiments, sequencing is used to determine the presence of a mutation in the infertility-associated genetic region. In particularly-preferred embodiments, the sequencing is sequencing-by-synthesis.

In other embodiments, the biomarker is a gene product. In particular embodiments, the gene product is a product of an infertility-related gene. The gene product may be RNA or protein. Any assay known in the art may be used to analyze the gene product. In certain embodiments, the assay involves determining an amount of the gene product and comparing the determined amount to a reference.

Methods of the invention may further involve obtaining a sample from the mammal that includes the infertility-associated biomarker. The sample may be a human tissue or body fluid. In particular embodiments, the sample is blood or saliva. Methods of the invention may also involve enriching the sample for the infertility-associated biomarker.

Methods of the invention may be used to assess the risk of infertility that is linked to an infertility-associated biomarker. Another aspect of the invention provides methods for assessing infertility that involve obtaining a sample, conducting an assay on at least one infertility-associated biomarker, and assessing level of fertility based on results of the assay.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the rate of decline of fertility with age and the corresponding increase in the risk of infertility with age. The shades areas represent different age groups who would benefit from a genetic screen for infertility risk (late teen to mid 40's) versus a genetic screen of premature decline in fertility (late teens to late 30's).

FIG. 2 depicts one way that phenotypic variables can be utilized to accelerate the discovery of genetic regions related to female infertility.

FIG. 3 depicts the methodology for integrating clinical data with genomic data to predict treatment dependent and independent fertility outcomes.

FIG. 4 depicts the different kinds of genetic variants associated with risk of infertility.

FIG. 5 depicts the method for filtering through variants detected in whole genome sequencing for the identification of genetic regions related to infertility.

FIG. 6 depicts some of the components of the Fertilome™ Database, a tool for correlating genetic regions with risk for infertility (Fertilome™ Score).

FIG. 7 is the bioinformatics pipeline used to identify biologically interesting and statistically significant genetic variants in infertile patients.

FIG. 8 shows the different types of biologically or statistically significant genetic variants that were detected in infertile patients in the MUC4 genetic region.

FIG. 9 provides CGH array data of copy number variations associated with infertility.

FIG. 10 illustrates a specific copy number variation detected in the GJC2 gene of Chromosome 1.

FIG. 11 illustrates a specific copy number variation detected in the CRTC1 and GDF1 genes of Chromosome 19.

FIG. 12 illustrates a specific copy number variation detected in a non-coding region of Chromosome 6.

FIG. 13 illustrates population stratification correction of two patient groups (ZA=patients who did not get pregnant with IVF treatment, ZB=patients with infertility who did get pregnant with WF treatment).

FIG. 14 exemplifies a cluster analysis according to certain aspects.

FIG. 15 illustrates a system for implementing methods of the invention.

DETAILED DESCRIPTION

The invention generally relates to methods and devices for assessing risk of susceptibility to infertility, reduced fertility, or reduced fertility at a particular age including premature menopause. In certain embodiments, the invention provides methods for assessing risk of susceptibility to infertility or reduced fertility that involve obtaining a biological sample, conducting an assay on at least one infertility-associated biomarker, and assessing risk to of infertility or reduced fertility based upon results of the assay.

Samples

Methods of the invention involve obtaining a sample, e.g., a tissue or body fluid, that is suspected to include an infertility-associated gene or gene product. The sample may be collected in any clinically acceptable manner. A tissue is a mass of connected cells and/or extracellular matrix material, e.g. skin tissue, hair, nails, endometrial tissue, nasal passage tissue, CNS tissue, neural tissue, eye tissue, liver tissue, kidney tissue, placental tissue, mammary gland tissue, placental tissue, gastrointestinal tissue, musculoskeletal tissue, genitourinary tissue, bone marrow, and the like, derived from, for example, a human or other mammal and includes the connecting material and the liquid material in association with the cells and/or tissues. A body fluid is a liquid material derived from, for example, a human or other mammal. Such body fluids include, but are not limited to, mucous, blood, plasma, serum, serum derivatives, bile, maternal blood, phlegm, saliva, sweat, amniotic fluid, menstrual fluid, endometrial aspirates, mammary fluid, follicular fluid of the ovary, fallopian tube fluid, peritoneal fluid, urine, and cerebrospinal fluid (CSF), such as lumbar or ventricular CSF. A sample may also be a fine needle aspirate or biopsied tissue. A sample also may be media containing cells or biological material. A sample may also be a blood clot, for example, a blood clot that has been obtained from whole blood after the serum has been removed. In certain embodiments, infertility-associated genes or gene products may be found in reproductive cells or tissues, such as gametic cells, gonadal tissue, fertilized embryos, and placenta. In certain embodiments, the sample is drawn blood or saliva.

Nucleic acid is extracted from the sample according to methods known in the art. See for example, Maniatis, et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor, N.Y., pp. 280-281, 1982, the contents of which are incorporated by reference herein in their entirety. In certain embodiments, a genomic sample is collected from a subject followed by enrichment for genetic regions or genetic fragments of interest, for example by hybridization to a nucleotide array comprising fertility-related genes or gene fragments of interest. The sample may be enriched for genes of interest (e.g., infertility-associated genes) using methods known in the art, such as hybrid capture. See for examples, Lapidus (U.S. Pat. No. 7,666,593), the content of which is incorporated by reference herein in its entirety.

RNA may be isolated from eukaryotic cells by procedures that involve lysis of the cells and denaturation of the proteins contained therein. Tissue of interest includes gametic cells, gonadal tissue, endometrial tissue, fertilized embryos, and placenta. RNA may be isolated from fluids of interest by procedures that involve denaturation of the proteins contained therein. Fluids of interest include blood, menstrual fluid, mammary fluid, follicular fluid of the ovary, peritoneal fluid, or culture medium. Additional steps may be employed to remove DNA. Cell lysis may be accomplished with a nonionic detergent, followed by microcentrifugation to remove the nuclei and hence the bulk of the cellular DNA. In one embodiment, RNA is extracted from cells of the various types of interest using guanidinium thiocyanate lysis followed by CsCl centrifugation to separate the RNA from DNA (Chirgwin et al., Biochemistry 18:5294-5299 (1979)). Poly(A)+ RNA is selected by selection with oligo-dT cellulose (see Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, separation of RNA from DNA can be accomplished by organic extraction, for example, with hot phenol or phenol/chloroform/isoamyl alcohol. If desired, RNase inhibitors may be added to the lysis buffer. Likewise, for certain cell types, it may be desirable to add a protein denaturation/digestion step to the protocol.

For many applications, it is desirable to preferentially enrich mRNA with respect to other cellular RNAs, such as transfer RNA (tRNA) and ribosomal RNA (rRNA). Most mRNAs contain a poly(A) tail at their 3′ end. This allows them to be enriched by affinity chromatography, for example, using oligo(dT) or poly(U) coupled to a solid support, such as cellulose or SEPHADEX (see Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Once bound, poly(A)+ mRNA is eluted from the affinity column using 2 mM EDTA/0.1% SDS.

Biomarkers

A biomarker generally refers to a molecule that may act as an indicator of a biological state. Biomarkers for use with methods of the invention may be any marker that is associated with infertility. Exemplary biomarkers include genes (e.g. any region of DNA encoding a functional product), genetic regions (e.g. regions including genes and intergenic regions with a particular focus on regions conserved throughout evolution in placental mammals), and gene products (e.g., RNA and protein). In certain embodiments, the biomarker is an infertility-associated genetic region. An infertility-associated genetic region is any DNA sequence in which variation is associated with a change in fertility. Examples of changes in fertility include, but are not limited to, the following: a homozygous mutation of an infertility-associated gene leads to a complete loss of fertility; a homozygous mutation of an infertility-associated gene is incompletely penetrant and leads to reduction in fertility that varies from individual to individual; a heterozygous mutation is completely recessive, having no effect on fertility; and the infertility-associated gene is X-linked, such that a potential defect in fertility depends on whether a non-functional allele of the gene is located on an inactive X chromosome (Barr body) or on an expressed X chromosome.

According to certain aspects, methods of the invention provide for determining infertility genetic regions of interest based on data obtained from public and private fertility/infertility related databases. Infertility/fertility related data may include implantation genes, idiopathic infertility genes, polycystic ovary syndrome (PCOS) genes, egg quality genes, endometriosis genes, and premature ovarian failure genes. As described below, the infertility/fertility related data can then be processed using evolutionary conservation to identify genomic regions and variations of interest.

Evolutionary conservation analysis involves, generally, comparing nucleic acid sequences among evolutionary and distantly related genomes to identify similarities and differences between coding and/or non-coding regions across the genomes. The similarity between a region being examined and the related genomes correlates to a degree of conservation. Regions (e.g. coding, non-coding regions, and intergenic regions flanking a gene) that maintain a high degree of similarity across genomes over time are considered highly conserved. Differences between the examined region and regions of related genomes indicate that the examined region has evolved over time. If the examined region is conserved among related genomes, the region is generally considered to exhibit or perform functions that are important for the species (i.e. functionally relevant). This is because genetic abnormalities at functionally important regions are typically harmful to the species, and are phased out over the evolutionary time span. Because functional elements are subject to selection, functional regions tend to evolve at slower rates than nonfunctional regions. A degree of conservation (e.g. degree of similarity between a target genomic region and related genomes) that is considered to be functionally relevant depends on the particular application. For example, a functionally relevant degree of conservation may be 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% 97%, 98%, 99%, etc. Regions of genes identified by evolutionary conservation as being functionally-relevant can then be used as regions of interest for diagnosing diseases and disorders, such as infertility.

According to certain embodiments, infertility regions of interest are identified by performing evolutionary conservation analysis of one or more genes obtained from infertility and/or fertility-related data. The process of filtering through infertility/fertility related databases using evolutionary conservation, according to the invention, is called the ABCoRE algorithm (see FIG. 6). For example, nucleic acid data obtained from the infertility/fertility related databases can be compared to distantly related genomes in order to assess conservation of the infertility-related nucleic acid. Regions of the nucleic acid determined to be conserved are classified as infertility regions of interest. In one embodiment, methods of the invention assess conservation of coding regions to determine infertility regions of interest. In another embodiment, methods of the invention assess conservation of non-coding regions to determine infertility regions of interest. In further embodiments, methods of the invention assess conservation of intergenic regions (i.e. a non-coding region flanking a gene) to determine infertility regions of interest. In other embodiments, conversation of both coding and non-coding regions is assessed to determine infertility regions of interest. In any of the above embodiments, coding, non-coding, and intergenic regions may be classified as an infertility region of interest if they have a degree of conservation of, for example, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% 97%, 98%, 99%, etc.

In particular aspects, the following method is employed to determine whether a genomic region is a fertility region of interest using conservation analysis. First, private and/or public nucleic acid data corresponding to infertility or fertility is obtained. Next, one or more genetic loci from that data is examined for conservation. The coding regions (i.e. exons)) of a gene, non-coding regions of the gene, and/or regions flanking the gene (intergenic regions upstream and downstream from the gene being examined) are then analyzed for conservation. According to certain embodiments, if the coding region is found to be conserved (e.g. a degree of conservation 90% or above), the coding region is considered to be an infertility region of interest. The degree of conservation of the non-coding region is then compared to the degree of conservation of the coding region. If the degree of conservation of the non-coding region is similar to the degree of conversation of the coding region, then the non-coding region is also classified an infertility region of interest. This degree of conservation comparison may also be used to determine whether intergenic regions flanking a gene should be classified as an infertility region of interest.

Conservation of coding and/or non-coding sequences is described in Hardison, R. C., Oeltjen, J., and Miller, W. 1997. Long human-mouse sequence alignments reveal novel regulatory elements: A reason to sequence the mouse genome. Genome Res. 7: 959-966; Brenner, S., Venkatesh, B., Yap, W. H., Chou, C. F., Tay, A., Ponniah, S., Wang, Y., and Tan, Y. H. 2002. Conserved regulation of the lymphocyte-specific expression of lck in the Fugu and mammals. Proc. Natl. Acad. Sci. 99: 2936-2941; Karolchik, Donna, et al. “Comparative genomic analysis using the UCSC genome browser.” Comparative Genomics. Humana Press, 2008. 17-33; Santini, Simona, Jeffrey L. Boore, and Axel Meyer. “Evolutionary conservation of regulatory elements in vertebrate Hox gene clusters.” Genome research 13.6a (2003): 1111-1122; Roth, F. P., Hughes, J. D., Estep, P. W., and Church, G. M. 1998. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol. 16: 939-945; and Blanchette, M. and Tompa, M. 2002. Discovery of regulatory elements by a computational method for phylogenetic footprinting. Genome Res. 12: 739-748.

In particular embodiments, the infertility-associated genetic region is a maternal effect gene. Maternal effects genes are genes that have been found to encode key structures and functions in mammalian oocytes (Yurttas et al., Reproduction 139:809-823, 2010). Maternal effect genes are described, for example in, Christians et al. (Mol Cell Biol 17:778-88, 1997); Christians et al., Nature 407:693-694, 2000); Xiao et al. (EMBO J. 18:5943-5952, 1999); Tong et al. (Endocrinology 145:1427-1434, 2004); Tong et al. (Nat Genet. 26:267-268, 2000); Tong et al. (Endocrinology, 140:3720-3726, 1999); Tong et al. (Hum Reprod 17:903-911, 2002); Ohsugi et al. (Development 135:259-269, 2008); Borowczyk et al. (Proc Natl Acad Sci USA., 2009); and Wu (Hum Reprod 24:415-424, 2009). The content of each of these is incorporated by reference herein in its entirety.

The above-described infertility genetic regions of interest may then be ranked according to significance using one or more the following ranking schemes of the invention.

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 1 below. In Table 1, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 1 below depicts one possible gene ranking scheme for the relative infertility, subfertility, or premature decline in fertility risk associated with novel or common mutations or variants in a fertility gene. The number of varients column corresponds to the experimental observations of these variants in a study of women with unexplained infertility. The most highly ranked (from top to bottom) genes in this list contained the most varients that were predicted to significantly affect protein structure and function (biologically significant) out of a list of fertility related genes. Genetic variants considered to be biologically significant include mutations that result in a change: 1) to a different amino acid predicted to alter the folding and/or structure of the encoded protein, 2) to a different amino acid occurring at a site with high evolutionarily conservation in mammals, 3) that introduces a premature stop termination signal, 4) that causes a stop termination signal to be lost, 5) that introduces a new start codon, 6) that causes a start codon to be lost, 7) that disrupts a splicing signal, 8) that alters the reading frame or 9) that alters the dosage of encoded protein or RNA. All genetic variants detected from re-sequencing exclude sites where the variant allele is detected in only one chromosome (singletons) and sites sequenced in only one individual.

TABLE 1 Genomic loci containing biologically significant mutations ranked based on number of biologically significant variants observed in a study of unexplained female infertility. Num- ber of Var- Variant iants Description Celmatix HGNC de- (type and Gene Gene ID Entrez ID ID tected count) MUC4 CMX- 4585 7514 353 Drastic G0000006719 nonsynon- ymous: 352; Start codon gained: 1 EPHA8 CMX- 2046 3391 23 CNV loss: G0000000415 23  LOXL4 CMX- 84171 17171 11 CNV loss: G0000016263 11  FGF8 CMX- 2253 3686 4 CNV gain: G0000016316 4 KISS1R CMX- 84634 4510 4 CNV gain: G0000026560 4 SCARB1 CMX- 949 1664 4 Drastic G0000019991 nonsynon- ymous: 1; Start codon gained: 3 BARD1 CMX- 580 952 3 Drastic G0000004834 nonsynon- ymous: 1; Start codon gained: 1 Start codon lost: 1 DDX20 CMX- 11218 2743 3 Start codon G0000001412 gained: 3 ECHS1 CMX- 1892 3151 3 CNV gain: G0000016594 2, CNV loss: 1 FMN2 CMX- 56776 14074 3 Start codon G0000002910 gained: 3 FOXO3 CMX- 2309 3821 3 CNV gain: G0000010672 3 HS6ST1 CMX- 9394 5201 3 Drastic G0000004221 nonsynon- ymous: 3 MAP3K2 CMX- 10746 6854 3 CNV gain: G0000004205 3 MST1 CMX- 4485 7380 3 Drastic G0000005619 nonsynon- ymous: 2 Splice site acceptor: 1 MTRR CMX- 4552 7473 3 Drastic G0000008130 nonsynon- ymous: 3 NLRP11 CMX- 204801 22945 3 Drastic G0000028188 nonsynon- ymous: 2; Start codon gained: 1 NLRP14 CMX- 338323 22939 3 Drastic G0000016919 nonsynon- ymous: 3 NLRP8 CMX- 126205 22940 3 Drastic G0000028191 nonsynon- ymous: 2; Stop codon lost: 1 ASCL2 CMX- 430 739 2 Start codon G0000016707 gained: 1 CNV gain: 1 BMP6 CMX- 654 1073 2 CNV loss: G0000009564 2 BRCA1 CMX- 672 1100 2 Drastic G0000025305 nonsynon- ymous: 2 BRCA2 CMX- 675 1101 2 Drastic G0000020222 nonsynon- ymous: 2 CENPI CMX- 2491 3968 2 Start codon G0000031175 gained: 2 COMT CMX- 1312 2228 2 Drastic G0000029621 nonsynon- ymous: 1; Start codon gained: 1 CYP11B1 CMX- 1584 2591 2 CNV gain: G0000013888 2 DAZL CMX- 1618 2685 2 Start codon G0000005296 gained: 2 EEF1A1 CMX- 1915 3189 2 Start codon G0000010487 gained: 2 FMR1 CMX- 2332 3775 2 Drastic G0000031614 nonsynon- ymous: 1; Start codon gained: 1 GDF1 CMX- 2657 4214 2 Drastic G0000027183 nonsynon- ymous: 1; CNV gain: 1 HK3 CMX- 3101 4925 2 Drastic G0000009361 nonsynon- ymous: 2 IGF2 CMX- 3481 5466 2 CNV gain: G0000016702 2 ISG15 CMX- 9636 4053 2 CNV gain: G0000000029 2 JMY CMX- 133746 28916 2 Drastic G0000008593 nonsynon- ymous: 2 KL CMX- 9365 6344 2 Drastic G0000020228 nonsynon- ymous: 2 MTHFR CMX- 4524 7436 2 Drastic G0000000213 nonsynon- ymous: 1; Start codon gained: 1 NLRP13 CMX- 126204 22937 2 Drastic G0000028190 nonsynon- ymous: 2 NLRP5 CMX- 126206 21269 2 Drastic G0000028192 nonsynon- ymous: 2 NOBOX CMX- 135935 22448 2 Drastic G0000012690 nonsynon- ymous: 2 PRKRA CMX- 8575 9438 2 Drastic G0000004587 nonsynon- ymous: 1; Nonsynon- ymous start: 1 SDC3 CMX- 9672 10660 2 Drastic G0000000574 nonsynon- ymous: 2 TACC3 CMX- 10460 11524 2 Drastic G0000006818 nonsynon- ymous: 2 TLE6 CMX- 79816 30788 2 CNV loss: G0000026639 2 ACVR1C CMX- 130399 18123 1 Drastic G0000004406 nonsynon- ymous: 1 AHR CMX- 196 348 1 Start codon G0000011332 gained: 1 APOA1 CMX- 335 600 1 CNV gain: G0000018327 1 AURKA CMX- 6790 11393 1 Start codon G0000028967 gained: 1 BMP15 CMX- 9210 1068 1 CNV gain: G0000030783 1 BMP4 CMX- 652 1071 1 Stop codon G0000021216 lost: 1 C6orf221 CMX- 154288 33699 1 Drastic G0000010478 nonsynon- ymous: 1 CASP8 CMX- 841 1509 1 CNV loss: G0000004721 1 CBS CMX- 875 1550 1 Drastic G0000029408 nonsynon- ymous: 1 CDX2 CMX- 1045 1806 1 Drastic G0000020191 nonsynon- ymous: 1 CENPF CMX- 1063 1857 1 Drastic G0000002670 nonsynon- ymous: 1 CGB CMX- 1082 1886 1 Start codon G0000027860 gained: 1 CSF1 CMX- 1435 2432 1 CNV loss: G0000001374 1 CSF2 CMX- 1437 2434 1 CNV loss: G0000008885 1 DCTPP1 CMX- 79077 28777 1 CNV gain: G0000023705 1 DNMT1 CMX- 1786 2976 1 Drastic G0000026880 nonsynon- ymous: 1 EFNA4 CMX- 1945 3224 1 CNV loss: G0000001896 1 EFNB3 CMX- 1949 3228 1 CNV gain: G0000024616 1 EIF3CL CMX- 728689 26347 1 CNV loss: G0000023621 1 EPHA5 CMX- 2044 3389 1 CNV loss: G0000007213 1 EPHA7 CMX- 2045 3390 1 CNV loss: G0000010603 1 EZH2 CMX- 2146 3527 1 Drastic G0000012702 nonsynon- ymous: 1 FOXL2 CMX- 668 1092 1 Start codon G0000006297 gained: 1 FOXP3 CMX- 50943 6106 1 CNV gain: G0000030750 1 GALT CMX- 2592 4135 1 Splice site G0000014248 acceptor: 1 GDF9 CMX- 2661 4224 1 Start codon G0000008902 gained: 1 GJA4 CMX- 2701 4278 1 CNV gain: G0000000643 1 GJB3 CMX- 2707 4285 1 CNV gain: G0000000642 1 GJB4 CMX- 127534 4286 1 CNV gain: G0000000641 1 GJD3 CMX- 125111 19147 1 CNV gain: G0000025169 1 GPC3 CMX- 2719 4451 1 CNV gain: G0000031486 1 HSD17B2 CMX- 3294 5211 1 Drastic G0000024260 nonsynon- ymous: 1 IGFBPL1 CMX- 347252 20081 1 CNV loss: G0000014341 1 KISS1 CMX- 3814 6341 1 CNV gain: G0000002533 1 LHCGR CMX- 3973 6585 1 Drastic G0000003462 nonsynon- ymous: 1 MAD1L1 CMX- 8379 6762 1 Start codon G0000011200 gained: 1 MAD2L1 CMX- 4085 6763 1 Start codon G0000007650 gained: 1 MB21D1 CMX- 115004 21367 1 Drastic G0000010484 nonsynon- ymous: 1 MCM8 CMX- 84515 16147 1 Drastic G0000028433 nonsynon- ymous: 1 MYC CMX- 4609 7553 1 Start codon G0000013826 gained: 1 NLRP2 CMX- 55655 22948 1 Start codon G0000028140 gained: 1 NLRP4 CMX- 147945 22943 1 Start codon G0000028189 gained: 1 OAS1 CMX- 4938 8086 1 Splice site G0000019838 acceptor: 1 PADI3 CMX- 51702 18337 1 CNV gain: G0000000342 1 PAEP CMX- 5047 8573 1 CNV gain: G0000015254 1 PLCB1 CMX- 23236 15917 1 CNV gain: G0000028445 1 PMS2 CMX- 5395 9122 1 Drastic G0000011251 nonsynon- ymous: 1 POF1B CMX- 79983 13711 1 CNV gain: G0000031099 1 PRDM9 CMX- 56979 13994 1 CNV loss: G0000008219 1 SEPHS2 CMX- 22928 19686 1 CNV gain: G0000023707 1 SERPINA10 CMX- 51156 15996 1 CNV gain: G0000021629 1 SIRT3 CMX- 23410 14931 1 CNV loss: G0000016629 1 SPN CMX- 101929889 11249 1 CNV loss: G0000023664 1 TFPI CMX- 7035 11760 1 Drastic G0000004632 nonsynon- ymous: 1 TGFB1I1 CMX- 7041 11767 1 CNV gain: G0000023757 1 TP63 CMX- 8626 15979 1 Start codon G0000006674 gained: 1 UBE3A CMX- 7337 12496 1 Start codon G0000022200 gained: 1 UBL4B CMX- 164153 32309 1 CNV loss: G0000001378 1 UIMC1 CMX- 51720 30298 1 Drastic G0000009362 nonsynon- ymous: 1 VKORC1 CMX- 79001 23663 1 CNV gain: G0000023741 1 ZP3 CMX- 7784 13189 1 Start codon G0000011947 gained: 1

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 2 below. In Table 2, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 2 below depicts another possible gene ranking scheme for the relative infertility, subfertility, or premature decline in fertility risk associated with novel or common mutations or variants in a fertility gene. Table 2 contains the 10 genes, listed in order from most to least statistically significant, that were determined to be statistically signifcantly correlated with infertility risk in a study of unexplained female infertilty based on variants detected in the coding regions of these genes. P-values<0.025 are considered statistically significant, and all other fertility genes did not fit the pass the significance test for inclusion and ranking in this list. For the coding level analysis, we first compute a coding variant score for the coding regions for each individual/gene. The coding variant score represents the variability of the gene at coding regions in an individual and is computed as the sum of the proportion of variant locations within the coding regions of that gene for that individual. A series of linear regression models are fit, where the outcome variable is the coding variant score for a given gene, and the independent variables are group (infertile vs control) and principal component derived ethnicity (continuous). The p-value for group is used for statistical inference. The model is fit once for each gene.

TABLE 2 Fertility genes demonstrating statistical significance at the gene coding region level for infertility risk ranked based on p-values, observed in a study of unexplained female infertility. Celmatix Gene Gene ID Entrez ID HGNC ID P-value ZP4 CMX- 57829 15770 5.17E−10 G0000002903 UIMC1 CMX- 51720 30298 0.001401803 G0000009362 PADI6 CMX- 353238 20449 0.003420271 G0000000344 ZP1 CMX-G0000017558 22917 13187 0.003845858 MDM2 CMX- 4193 6973 0.009323844 G0000019503 PRKRA CMX- 8575 9438 0.009832035 G0000004587 PMS2 CMX-G0000011251 5395 9122 0.015453858 TGFB1 CMX- 7040 11766 0.018576967 G0000027588 ESR2 CMX- 2100 3468 0.022661688 G0000021326 PRDM1 CMX- 639 9346 0.024522163 G0000010653

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 3 below. In Table 3, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 3 below depicts another possible gene ranking scheme for the relative infertility, subfertility, or premature decline in fertility risk associated with novel or common mutations or variants in a fertility gene. Table 3 contains the 11 genes, listed in order from most to least statistically significant, that were determined to be statistically signifcantly correlated with infertility risk in a study of unexplained female infertilty based on variants detected in the coding, non-coding, and conserved upstream and downstream regions of the fertility gene. P-values<0.025 are considered statistically significant, and all other fertility genes did not fit the pass the significance test for inclusion and ranking in this list. For the gene level analysis, we first compute a gene variant score for the entire transcript and flanking evolutionarily conserved regions for each individual/gene. The gene variant score represents the variability of the gene in an individual and is computed as the sum of the proportion of variant locations within that gene and its evolutionarily conserved regions flanking the gene for that individual. A series of linear regression models are fit, where the outcome variable is the gene variant score for a given gene, and the independent variables are group (infertile vs control) and principal component derived ethnicity (continuous). The p-value for group is used for statistical inference. The model is fit once for each gene.

TABLE 3 Fertility genes demonstrating statistical significance at the entire gene level for infertility risk ranked based on p-values, observed in a study of unexplained female infertility. Gene Celmatix Gene ID Entrez ID HGNC ID P-value PADI6 CMX-G0000000344 353238 20449 0.00079599 CGB CMX-G0000027860 1082 1886 0.000983714 PMS2 CMX-G0000011251 5395 9122 0.001500248 ESR2 CMX-G0000021326 2100 3468 0.004733531 UIMC1 CMX-G0000009362 51720 30298 0.005170633 ZP1 CMX-G0000017558 22917 13187 0.00852914 MDM2 CMX-G0000019503 4193 6973 0.009794758 BRCA2 CMX-G0000020222 675 1101 0.019744499 TGFB1 CMX-G0000027588 7040 11766 0.020358934 CDKN1C CMX-G0000016717 1028 1786 0.022605239 TAF4B CMX-G0000026229 6875 11538 0.024673723

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 4 below. In Table 4, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 4 below depicts another possible gene ranking scheme for the relative infertility, subfertility, or premature decline in fertility risk associated with novel or common mutations or variants in a fertility gene. Table 4 contains the top ranked 100 fertility genes, listed in order from most to least likely for variants in that gene to affect fertility. Genes are ranked according to a Celmatix Fertilome™ Score, G1Version2, that reflects the likelihood a gene is involved in fertility or reproduction. This score is computed using a database of mined and curated data, containing attributes for each gene in the genome (See FIGS. 5 and 6). These attributes include: diseases and disorders related to infertility, molecular pathways, molecular interactions, gene clusters, mouse phenotypes associated with each gene, gene expression data in reproductive tissues, proteomics data in oocytes, and accrued information from scientific publications through text-mining.

The process for ranking fertility-related attributes of a gene or genetic region (locus) to obtain an infertility score is called the SESMe algorithm. The SESMe algorithm is applied to a database of features and attributes that might make a particular gene important for fertility. The algorithm assigns a score and a relative weight to each feature then ranks genetic regions from most to least important (or vice versa) by weighting features and attributes associated with that genetic region. For example, a score is assigned to a gene by compiling the combined weighted values of attributes associated with that gene. After each gene is scored based on its weighted attributes, the genes can be ranked in order of importance in accordance with their score. The weighted value for each infertility attribute may be scaled in any manner including and not limited to assigning a positive or negative integer to reflect the significance or severity of the attribute to infertility.

In certain embodiments, the weighted value for gene infertility attributes may be on a scale from −10 to +10. A +10 may indicate that an attribute of a gene being scored is highly associated with infertility because that attribute is prevalently found in infertile patient populations. A +4 may represent an attribute that is a latent infertility marker, meaning it will not cause infertility on its own, but may lead to infertility upon influence of external factors such as aging and smoking. Whereas +2 may represent an attribute found in some infertile patients but nothing directly relates the attribute to infertility. A zero on the scale may include an attribute not yet known to have any effect or any negative effect towards infertility. A −10 may include an attribute shown not to affect infertility whatsoever. Further, embodiments provide for the weighted scale to include a +1 for attributes that are commonly found in infertile patient populations, 0.5 for attributes similar to those found in infertile patient populations, and 0 for attributes without a causal link to infertility.

In addition, weighted values for attributes may be normalized based on the known significance of that attribute towards infertility. For example and in certain embodiments, when scoring attributes of a particular gene, each attribute may be assigned a 0 if the attribute is absent and a 1 if the attribute is present. The attributes may then be normalized based on the infertility significance of that attribute. For example, if the attribute is a genetic mutation known to be associated with infertility, then that attribute may be normalized by a factor of 5. In another example, if the attribute is a signaling pathway defect sometimes associated with infertility, then that attribute may be normalized by a factor of 2.

Table 4, provided below, lists 100 Human Fertility Genes that were ranked by weighing attributes associated with the gene in accordance with methods of the invention.

TABLE 4 List of Top 100 Human Fertility Genes based on the Fertilome ™Score, G1Version2. Celmatix Gene Celmatix Entrez HGNC Fertilome ™ Rank Symbol Gene ID Gene ID Gene ID Score 1 C6orf221 CMX- 154288 33699 15 G0000010478 2 NLRP5 CMX- 126206 21269 15 G0000028192 3 ZP3 CMX- 7784 13189 12.93 G0000011947 4 FIGLA CMX- 344018 24669 12 G0000003616 5 PADI6 CMX- 353238 20449 12 G0000000344 6 DNMT1 CMX- 1786 2976 11.67 G0000026880 7 ZP2 CMX- 7783 13188 11.67 G0000023549 8 FSHR CMX- 2492 3969 11.37 G0000003464 9 OOEP CMX- 441161 21382 11 G0000010479 10 FOXO3 CMX- 2309 3821 10.39 G0000010672 11 ACVR1B CMX- 91 172 10.14 G0000019186 12 CGA CMX- 1081 1885 10.04 G0000010560 13 INHA CMX- 3623 6065 10.02 G0000004914 14 LHCGR CMX- 3973 6585 10.01 G0000003462 15 DPPA3 CMX- 359787 19199 10 G0000018719 16 KDM1B CMX- 221656 21577 10 G0000009642 17 NOBOX CMX- 135935 22448 10 G0000012690 18 NPM2 CMX- 10361 7930 10 G0000013114 19 ESR1 CMX- 2099 3467 9.91 G0000011002 20 AURKA CMX- 6790 11393 9.84 G0000028967 21 BRCA2 CMX- 675 1101 9.75 G0000020222 22 WT1 CMX- 7490 12796 9.53 G0000017126 23 CBS CMX- 875 1550 9.49 G0000029408 24 CDKN1C CMX- 1028 1786 9.37 G0000016717 25 IGF1 CMX- 3479 5464 9.35 G0000019714 26 HAND2 CMX- 9464 4808 9.17 G0000007954 27 GDF9 CMX- 2661 4224 9 G0000008902 28 MAD2L1 CMX- 4085 6763 9 G0000007650 29 ZAR1 CMX- 326340 21436 9 G0000007128 30 FOXL2 CMX- 668 1092 8.88 G0000006297 31 BARD1 CMX- 580 952 8.54 G0000004834 32 FMN2 CMX- 56776 14074 8.4 G0000002910 33 TACC3 CMX- 10460 11524 8.39 G0000006818 34 MYC CMX- 4609 7553 8.25 G0000013826 35 IL11RA CMX- 3590 5967 7.9 G0000014249 36 MCM8 CMX- 84515 16147 7.85 G0000028433 37 LHB CMX- 3972 6584 7.82 G0000027859 38 TAF4B CMX- 6875 11538 7.68 G0000026229 39 USP9X CMX- 8239 12632 7.67 G0000030612 40 PRLR CMX- 5618 9446 7.58 G0000008271 41 HSF1 CMX- 3297 5224 7.35 G0000013948 42 FSHB CMX- 2488 3964 7.33 G0000017113 43 ZP1 CMX- 22917 13187 7.29 G0000017558 44 MDM2 CMX- 4193 6973 7.27 G0000019503 45 BMP15 CMX- 9210 1068 7.25 G0000030783 46 GPC3 CMX- 2719 4451 7.11 G0000031486 47 PRDM1 CMX- 639 9346 7.05 G0000010653 48 FST CMX- 10468 3971 7 G0000008371 49 EZH2 CMX- 2146 3527 6.91 G0000012702 50 SMAD2 CMX- 4087 6768 6.89 G0000026329 51 NODAL CMX- 4838 7865 6.88 G0000015959 52 ACVR1 CMX- 90 171 6.81 G0000004407 53 HSD17B12 CMX- 51144 18646 6.71 G0000017190 54 BRCA1 CMX- 672 1100 6.67 G0000025305 55 DICER1 CMX- 23405 17098 6.53 G0000021645 56 ESR2 CMX- 2100 3468 6.47 G0000021326 57 MDM4 CMX- 4194 6974 6.42 G0000002542 58 AR CMX- 367 644 6.41 G0000030935 59 SCARB1 CMX- 949 1664 6.39 G0000019991 60 CDKN1B CMX- 1027 1785 6.25 G0000018846 61 TP53 CMX- 7157 11998 6.23 G0000024614 62 NOG CMX- 9241 7866 6.22 G0000025542 63 IL6ST CMX- 3572 6021 6.13 G0000008398 64 DAZL CMX- 1618 2685 6 G0000005296 65 NLRP11 CMX- 204801 22945 6 G0000028188 66 NLRP13 CMX- 126204 22937 6 G0000028190 67 NLRP8 CMX- 126205 22940 6 G0000028191 68 NLRP9 CMX- 338321 22941 6 G0000028184 69 ZFX CMX- 7543 12869 5.67 G0000030503 70 TFPI CMX- 7035 11760 5.36 G0000004632 71 HSD17B7 CMX- 51478 5215 5.32 G0000002148 72 TP63 CMX- 8626 15979 5.28 G0000006674 73 NR5A1 CMX- 2516 7983 5.24 G0000015051 74 BMP7 CMX- 655 1074 5.09 G0000028985 75 CGB CMX- 1082 1886 5 G0000027860 76 CGB5 CMX- 93659 16452 5 G0000027866 77 DDX43 CMX- 55510 18677 5 G0000010483 78 FMR1 CMX- 2332 3775 5 G0000031614 79 LIN28B CMX- 389421 32207 5 G0000010647 80 NLRP14 CMX- 338323 22939 5 G0000016919 81 NLRP4 CMX- 147945 22943 5 G0000028189 82 NLRP7 CMX- 199713 22947 5 G0000028139 83 PROK1 CMX- 84432 18454 5 G0000001385 84 SPIN1 CMX- 1927 11243 5 G0000014689 85 TFPI2 CMX- 7980 11761 5 G0000012044 86 ZP4 CMX- 57829 15770 5 G0000002903 87 ESRRB CMX- 2103 3473 4.8 G0000021489 88 UBE3A CMX- 7337 12496 4.76 G0000022200 89 SUZ12 CMX- 23512 17101 4.73 G0000025003 90 XIST CMX- 7503 12810 4.7 G0000031023 91 ATM CMX- 472 795 4.62 G0000018234 92 AURKB CMX- 9212 11390 4.55 G0000024639 93 STK3 CMX- 6788 11406 4.52 G0000013673 94 POLG CMX- 5428 9179 4.51 G0000023009 95 CDX2 CMX- 1045 1806 4.46 G0000020191 96 TP73 CMX- 7161 12003 4.43 G0000000110 97 MTOR CMX- 2475 3942 4.42 G0000000201 98 AHR CMX- 196 348 4.41 G0000011332 99 LIF CMX- 3976 6596 4.38 G0000029949 100 PRKRA CMX- 8575 9438 4.38 G0000004587

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 5 below. In Table 5, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 5 below depicts another possible gene ranking scheme for the relative infertility, subfertility, or premature decline in fertility risk associated with novel or common mutations or variants in a fertility gene. Table 5 contains the top ranked 100 fertility genes, listed in order from most to least likely for variants in that gene to affect fertility. Genes are ranked according to a Celmatix Fertilome™ Score, G1Version3, that reflects the likelihood a gene is involved in fertility or reproduction. This score is computed using a database of mined and curated data, containing attributes for each gene in the genome (See FIGS. 5 and 6). These attributes include: diseases and disorders related to infertility, molecular pathways, molecular interactions, gene clusters, mouse phenotypes associated with each gene, gene expression data in reproductive tissues, proteomics data in oocytes, and accrued information from scientific publications through text-mining. The Celmatix Fertilome™ Score, G1Version3 differs from G1Version2 (Table 4) because it contains more fertility genes as an input for the score calculation.

TABLE 5 List of Top 100 Human Fertility Genes based on the Fertilome ™Score, G1Version3. Entrez Gene HGNC Celmatix Fertilome ™ Rank Gene Symbol Celmatix Gene ID ID Gene ID Score 1 C6orf221 CMX-G0000010478 154288 33699 15 2 NLRP5 CMX-G0000028192 126206 21269 15 3 TCL1A CMX-G0000021654 8115 11648 14 4 ZP3 CMX-G0000011947 7784 13189 12.93 5 FIGLA CMX-G0000003616 344018 24669 12 6 PADI6 CMX-G0000000344 353238 20449 12 7 RSPO1 CMX-G0000000687 284654 21679 12 8 EPHA1 CMX-G0000012650 2041 3385 11.82 9 DNMT1 CMX-G0000026880 1786 2976 11.67 10 ZP2 CMX-G0000023549 7783 13188 11.67 11 MOS CMX-G0000013392 4342 7199 11.5 12 FSHR CMX-G0000003464 2492 3969 11.37 13 OOEP CMX-G0000010479 441161 21382 11 14 CUL1 CMX-G0000012701 8454 2551 10.67 15 HSP90B1 CMX-G0000019724 7184 12028 10.57 16 FOXO3 CMX-G0000010672 2309 3821 10.39 17 KISS1 CMX-G0000002533 3814 6341 10.21 18 ACVR1B CMX-G0000019186 91 172 10.14 19 CGA CMX-G0000010560 1081 1885 10.04 20 INHA CMX-G0000004914 3623 6065 10.02 21 LHCGR CMX-G0000003462 3973 6585 10.01 22 DPPA3 CMX-G0000018719 359787 19199 10 23 KDM1B CMX-G0000009642 221656 21577 10 24 NOBOX CMX-G0000012690 135935 22448 10 25 NPM2 CMX-G0000013114 10361 7930 10 26 PRMT3 CMX-G0000017073 10196 30163 10 27 GJA4 CMX-G0000000643 2701 4278 9.92 28 ESR1 CMX-G0000011002 2099 3467 9.91 29 SFRP4 CMX-G0000011506 6424 10778 9.89 30 AURKA CMX-G0000028967 6790 11393 9.84 31 BRCA2 CMX-G0000020222 675 1101 9.75 32 WT1 CMX-G0000017126 7490 12796 9.53 33 CBS CMX-G0000029408 875 1550 9.49 34 CDKN1C CMX-G0000016717 1028 1786 9.37 35 IGF1 CMX-G0000019714 3479 5464 9.35 36 PLCB1 CMX-G0000028445 23236 15917 9.33 37 CEP290 CMX-G0000019604 80184 29021 9.3 38 MSH5 CMX-G0000010000 4439 7328 9.29 39 HAND2 CMX-G0000007954 9464 4808 9.17 40 GDF9 CMX-G0000008902 2661 4224 9 41 MAD2L1 CMX-G0000007650 4085 6763 9 42 TNFAIP6 CMX-G0000004377 7130 11898 9 43 ZAR1 CMX-G0000007128 326340 20436 9 44 FOXL2 CMX-G0000006297 668 1092 8.88 45 PCNA CMX-G0000028417 5111 8729 8.78 46 YBX2 CMX-G0000024578 51087 17948 8.57 47 BARD1 CMX-G0000004834 580 952 8.57 48 AMBP CMX-G0000014963 259 453 8.4 49 FMN2 CMX-G0000002910 56776 14074 8.4 50 NCOA2 CMX-G0000013477 10499 7669 8.4 51 TEX12 CMX-G0000018279 56158 11734 8.4 52 TACC3 CMX-G0000006818 10460 11524 8.39 53 PGR CMX-G0000018173 5241 8910 8.37 54 FANCC CMX-G0000014774 2176 3584 8.25 55 MYC CMX-G0000013826 4609 7553 8.25 56 FGF8 CMX-G0000016316 2253 3686 8.23 57 SMAD5 CMX-G0000008943 4090 6771 8.12 58 CCS CMX-G0000017793 9973 1613 8 59 MSH4 CMX-G0000001108 4438 7327 8 60 SPO11 CMX-G0000028986 23626 11250 8 61 SYCE1 CMX-G0000016602 93426 28852 8 62 SYCP1 CMX-G0000001457 6847 11487 8 63 TFAP2C CMX-G0000028982 7022 11744 8 64 WNT7A CMX-G0000005260 7476 12786 7.96 65 IL11RA CMX-G0000014249 3590 5967 7.9 66 MCM8 CMX-G0000028433 84515 16147 7.85 67 SYCP2 CMX-G0000029020 10388 11490 7.85 68 INHBA CMX-G0000011550 3624 6066 7.83 69 MGAT1 CMX-G0000009451 4245 7044 7.83 70 LHB CMX-G0000027859 3972 6584 7.82 71 CYP19A1 CMX-G0000022537 1588 2594 7.74 72 GGT1 CMX-G0000029874 2678 4250 7.71 73 TAFB4 CMX-G0000026229 6875 11538 7.68 74 SMC1B CMX-G0000030247 27127 11112 7.67 75 USP9X CMX-G0000030612 8239 12632 7.67 76 PRLR CMX-G0000008271 5618 9446 7.58 77 DNMT3B CMX-G0000028640 1789 2979 7.54 78 SOD1 CMX-G0000029263 6647 11179 7.54 79 SH2B1 CMX-G0000023639 25970 30417 7.5 80 HOXA11 CMX-G0000011417 3207 5101 7.48 81 UBB CMX-G0000024729 7314 12463 7.43 82 HSF1 CMX-G0000013948 3297 5224 7.35 83 CYP17A1 CMX-G0000016340 1586 2593 7.33 84 FSHB CMX-G0000017113 2488 3964 7.33 85 SYCP3 CMX-G0000019706 50511 18130 7.33 86 NOS3 CMX-G0000012751 4846 7876 7.31 87 ZP1 CMX-G0000017558 22917 13187 7.29 88 GNRHR CMX-G0000007221 2798 4421 7.27 89 MDM2 CMX-G0000019503 4193 6973 7.27 90 BMP15 CMX-G0000030783 9210 1068 7.25 91 KDM1A CMX-G0000000422 23028 29079 7.25 92 MDK CMX-G0000017221 4192 6972 7.21 93 MSX2 CMX-G0000009331 4488 7392 7.21 94 CTNNB1 CMX-G0000005462 1499 2514 7.2 95 NRIP1 CMX-G0000029160 8204 8001 7.2 96 UBC CMX-G0000019992 7316 12468 7.2 97 FKBP4 CMX-G0000018615 2288 3720 7.19 98 MLH3 CMX-G0000021470 27030 7128 7.14 99 MSX1 CMX-G0000006873 4487 7391 7.13 100 GPC3 CMX-G0000031486 2719 4451 7.11

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 6 below. In Table 5, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 6 below depicts another possible gene ranking scheme for the relative infertility, subfertility, or premature decline in fertility risk associated with novel or common mutations or variants in a fertility gene. Table 6 contains the top ranked fertility genes based on a comparison of how often the gene appears in one of the lists above (Tables 1-5). This list represents the top 20 genetic regions with utility for diagnosing female infertility, subfertility, or premature decline in fertility. These targets were identified using a compendium of factors: 1) Carrying statistically significant genetic mutations at the coding level in a pilot study, 2) Carrying statistically significant genetic mutations at the coding level in a pilot study, 3) Carrying genetic variations in our pilot study that impact the biochemical properties of the gene, 4) Highly ranked in our Celmatix Fertilome™ Score system, that reflects the likelihood a gene is involved in fertility or reproduction.

TABLE 6 List of the Top 20 Fertility Genes (arranged in alphabetical order) Gene Symbol Celmatix Gene ID Entrez Gene ID HGNC Gene ID BARD1 CMX-G0000004834 580 952 C6orf221 CMX-G0000010478 154288 33699 DNMT1 CMX-G0000026880 1786 2976 FMR1 CMX-G0000031614 2332 3775 FOXO3 CMX-G0000010672 2309 3821 MUC4 CMX-G0000006719 4585 7514 NLRP11 CMX-G0000028188 204801 22945 NLRP14 CMX-G0000016919 338323 22939 NLRP5 CMX-G0000028192 126206 21269 NLRP8 CMX-G0000028191 126205 22940 NPM2 CMX-G0000013114 10361 7930 PADI6 CMX-G0000000344 353238 20449 PMS2 CMX-G0000011251 5395 9122 SCARB1 CMX-G0000019991 949 1664 SPIN1 CMX-G0000014689 10927 11243 TACC3 CMX-G0000006818 10460 11524 ZP1 CMX-G0000017558 22917 13187 ZP2 CMX-G0000023549 7783 13188 ZP3 CMX-G0000011947 7784 13189 ZP4 CMX-G0000002903 57829 15770

In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility selected from the genes shown in Table 7 below. In Table 7, HGNC (http://www.genenames.org/) reference numbers are provided when available.

Table 7 below depicts all of the biologically and/or statistically significant variants detected in the genes depicted in Table 6 in a genetic study of female infertility. Genetic variants considered to be biologically significant include mutations that result in a change: 1) to a different amino acid predicted to alter the folding and/or structure of the encoded protein, 2) to a different amino acid occurring at a highly evolutionarily conserved site, 3) that introduces a premature stop termination signal, 4) that causes a stop termination signal to be lost, 5) that introduces a new start codon, 6) that causes a start codon to be lost, 7) that disrupts a splicing signal, 8) that alters the reading frame or 9) that alters the dosage of encoded protein or RNA. All genetic variants detected from resequencing exclude sites at the single nucleotide level where the variant allele is detected in only one chromosome (singletons) and sites sequenced in only one individual. Structural variants impacting biological function are also reported. Using these criteria applied to targeted re-sequencing data from a study of infertile females, we detected 490 variants, of which 379 are listed in Table 7.

For the statistically significant variant level analysis, a series of logistic regression models are fit, where the outcome variable is the binary indicator of variant status for a given location, and the independent variables are group (infertile vs. control) and principal component-derived ethnicity (continuous). The p-value and odds ratio for group are used for statistical inference. The model is fit once for each location. P-values<0.001 are considered statistically significant. We performed a SNP association study by targeted re-sequencing and identified a total of 147 SNPs significantly associated with female infertility (of which 52 are reported in Table 7). Each variant was classified as novel or known. Novel sites are excluded from the p-value computation. For known variants, we apply a series of logistic regression models where the outcome variable is the binary indicator of variant status for a given location, and the independent variables are group (infertile vs. control) and principal component-derived ethnicity (continuous). The p-value and odds ratio for group are used for statistical inference. P-values less than 0.001 were considered significant. Position refers to NCBI Build 37. Alleles are reported on the forward strand. Ref=Reference allele, Alt=Variant allele.

TABLE 7 List of Biologically and Statistically Significant Genetic Variants Most Useful for Predicting Infertility Risk in Humans (arranged in alphabetical order by gene name) Celmatix Gene Celmatix Variant Symbol Gene ID ID Location Ref Alt Impact P-value APOA1 CMX- CMX- chr11:112553969- NA CNV APOA1 NA G0000018327 V1388879 126265772 gain (3 exons) ASCL2 CMX- CMX- chr11:2234334- NA CNV ASCL2 NA G0000016707 V1067111 2298706 gain (1 exon) BARD1 CMX- CMX- chr2:215674224 G A Drastic NA G0000004834 V9083698 nonsynonymous BARD1 CMX- CMX- chr2:215595645 C T Start codon NA G0000004834 V9083699 lost BARD1 CMX- CMX- chr2:215674323 C G Start codon NA G0000004834 V9083700 gained BARD1 CMX- CMX- chr2:215645502 GTGGTG G Codon NA G0000004834 SV00001 AAGAA deletion CATTCA GGCAA BARD1 CMX- CMX- chr2:215742204 G T NA 6.77E−05 G0000004834 V9084177 BMP15 CMX- CMX- chrX:50639969- NA CNV BMP15 NA G0000030783 V1250077 50981841 gain (2 exons) BMP6 CMX- CMX- chr6:7726514- NA CNV BMP6 NA G0000009564 V1247770 7727614 loss (1 exon) BMP6 CMX- CMX- chr6:7724859- NA CNV BMP6 NA G0000009564 V1166409 7728905 loss (1 exon) C6orf221 CMX- CMX- chr6:74073531 C G Drastic NA G0000010478 V9083706 nonsynonymous CASP8 CMX- CMX- chr2:201851129- NA CNV CASP8 NA G0000004721 V1843349 203110758 loss (2 exons) CSF1, CMX- CMX- chr1:110441465- NA CNV CSF1 NA UBL4B G0000001374, V1667025 110831379 loss (4 exons), CMX- UBL4B G0000001378 (1 exon) CSF2 CMX- CMX- chr5:128320218- NA CNV CSF2 NA G0000008885 V1456214 131440732 loss (4 exons) CYP11B1 CMX- CMX- chr8:143951813- NA CNV CYP11B1 NA G0000013888 V1957973 143958440 gain (4 exons) CYP11B1 CMX- CMX- chr8:143953403- NA CNV CYP11B1 NA G0000013888 V1609269 143991713 gain (4 exons) DCTPP1, CMX- CMX- chr16:30347689- NA CNV DCTPP1 NA SEPHS2, G0000023705, V1070550 31632796 gain (1 exon), TGFB1I1, CMX- SEPHS2 VKORC1 G0000023707, (1 exon), CMX- TGFB1I1 G0000023757, (3 exons), CMX- VKORC1 G0000023741 (1 exon) DNMT1 CMX- CMX- chr19:10291181 T C Drastic NA G0000026880 V9083720 nonsynonymous ECHS1 CMX- CMX- chr10:135087081- NA CNV ECHS1 NA G0000016594 V1101514 135243330 gain (8 exons) ECHS1 CMX- CMX- chr10:135088839- NA CNV ECHS1 NA G0000016594 V1131837 135243616 loss (8 exons) ECHS1 CMX- CMX- chr10:135087962- NA CNV ECHS1 NA G0000016594 V1335364 135243616 gain (8 exons) EFNA4 CMX- CMX- chr1:154354576- NA CNV EFNA4 NA G0000001896 V1267541 155066744 loss (4 exons) EFNB3 CMX- CMX- che17:7135639- NA CNV EFNB3 NA G0000024616 V1295730 7702377 gain (5 exons) EIF3CL CMX- CMX- chr16:28197032- NA CNV EIF3CL NA G0000023621 V1992389 28410526 loss (13 exons) EPHA5 CMX- CMX- chr4:66114884- NA CNV EPHA5 NA G0000007213 V1585842 66870165 loss (17 exons) EPHA7 CMX- CMX- chr6:94015504- NA CNV EPHA7 NA G0000010603 V1939194 95364976 loss (3 exons) EPHA8 CMX- CMX- chr1:22906197- NA CNV EPHA8 NA G0000000415 V1493926 22914076 loss (1 exon) EPHA8 CMX- CMX- chr1:22905731- NA CNV EPHA8 NA G0000000415 V1680494 22915711 loss (2 exons) EPHA8 CMX- CMX- chr1:22904786- NA CNV EPHA8 NA G0000000415 V1333389 22915711 loss (2 exons) EPHA8 CMX- CMX- chr1:22906271- NA CNV EPHA8 NA G0000000415 V1750787 22915711 loss (2 exons) EPHA8 CMX- CMX- chr1:22906197- NA CNV EPHA8 NA G0000000415 V1102470 22915711 loss (1 exon) EPHA8 CMX- CMX- chr1:22905731- NA CNV EPHA8 NA G0000000415 V1356293 22915352 loss (1 exon) EPHA8 CMX- CMX- chr1:22905731- NA CNV EPHA8 NA G0000000415 V1845595 22913963 loss (1 exon) EPHA8 CMX- CMX- chr1:22906526- NA CNV EPHA8 NA G0000000415 V1973671 22913011 loss (1 exon) EPHA8 CMX- CMX- chr1:22905731- NA CNV EPHA8 NA G0000000415 V1086453 22916983 loss (2 exons) EPHA8 CMX- CMX- chr1:22904856- NA CNV EPHA8 NA G0000000415 V1138079 22913700 loss (1 exon) EPHA8 CMX- CMX- chr1:22904786- NA CNV EPHA8 (1 NA G0000000415 V1957426 22914210 loss (1 exon) EPHA8 CMX- CMX- chr1:22906197- NA CNV EPHA8 (1 NA G0000000415 V1635641 22915352 loss (1 exon) EPHA8 CMX- CMX- chr1:22905731- NA CNV EPHA8 NA G0000000415 V1387198 22914256 loss (1 exon) EPHA8 CMX- CMX- chr1:22906271- NA CNV EPHA8 NA G0000000415 V1481340 22913750 loss (1 exon) EPHA8 CMX- CMX- chr1:22904856- NA CNV EPHA8 NA G0000000415 V1077862 22913963 loss (1 exon) EPHA8 CMX- CMX- chr1:22904064- NA CNV EPHA8 NA G0000000415 V1288029 22914256 loss (1 exon) EPHA8 CMX- CMX- chr1:22906395- NA CNV EPHA8 NA G0000000415 V1098423 22913750 loss (1 exon) EPHA8 CMX- CMX- chr1:22906271- NA CNV EPHA8 NA G0000000415 V1825294 22914210 loss (1 exon) EPHA8 CMX- CMX- chr1:22906271- NA CNV EPHA8 NA G0000000415 V1672255 22915161 loss (1 exon) EPHA8 CMX- CMX- chr1:22906271- NA CNV EPHA8 NA G0000000415 V1740010 22914076 loss (1 exon) EPHA8 CMX- CMX- chr1:22904856- NA CNV EPHA8 NA G0000000415 V1757241 22915352 loss (1 exon) EPHA8 CMX- CMX- chr1:22906322- NA CNV EPHA8 NA G0000000415 V1080982 22914695 loss (1 exon) EPHA8 CMX- CMX- chr1:22905731- NA CNV EPHA8 NA G0000000415 V1506728 22913502 loss (1 exon) FGF8 CMX- CMX- chr10:103524444- NA CNV FGF8 NA G0000016316 V1202186 103533748 gain (2 exons) FGF8 CMX- CMX- chr10:103524714- NA CNV FGF8 NA G0000016316 V1242750 103532892 gain (2 exons) FGF8 CMX- CMX- chr10:103520069- NA CNV FGF8 NA G0000016316 V1059642 103531134 gain (1 exon) FGF8 CMX- CMX- chr10:103525082- NA CNV FGF8 NA G0000016316 V1478224 103536399 gain (6 exons) FMR1 CMX- CMX- chrX:147010263 A C Drastic NA G0000031614 V9083727 nonsynonymous FMR1 CMX- CMX- chrX:147014960 C T Start codon NA G0000031614 V9083728 gained FMR1 CMX- CMX- chrX:146126483 G A NA 0.000198744 G0000031614 V9084252 FMR1 CMX- CMX- chrX:146153970 C T NA 1.92E−05 G0000031614 V9084253 FMR1 CMX- CMX- chrX:146195865 A G NA 0.000371198 G0000031614 V9084254 FMR1 CMX- CMX- chrX:146221514 C T NA 0.000292157 G0000031614 V9084255 FMR1 CMX- CMX- chrX:146247740 T A NA 0.0001997 G0000031614 V9084256 FMR1 CMX- CMX- chrX:146255213 G A NA 0.000185975 G0000031614 V9084257 FMR1 CMX- CMX- chrX:146406319 A G NA 0.000262855 G0000031614 V9084258 FMR1 CMX- CMX- chrX:146994916 A G NA 0.000816693 G0000031614 V9084259 FMR1 CMX- CMX- chrX:147002992 T G NA 0.000810806 G0000031614 V9084260 FMR1 CMX- CMX- chrX:147003339 A G NA 0.000810806 G0000031614 V9084261 FMR1 CMX- CMX- chrX:147003794 T C NA 0.000810806 G0000031614 V9084262 FMR1 CMX- CMX- chrX:147024558 A T NA 0.000641561 G0000031614 V9084263 FMR1 CMX- CMX- chrX:147372528 G C NA 0.000633948 G0000031614 V9084264 FMR1 CMX- CMX- chrX:147397806 A G NA 0.000813685 G0000031614 V9084265 FMR1 CMX- CMX- chrX:147437683 A G NA 0.000784981 G0000031614 V9084266 FMR1 CMX- CMX- chrX:147449673 T C NA 0.000401568 G0000031614 V9084267 FMR1 CMX- CMX- chrX:147454832 G A NA 0.000965078 G0000031614 V9084268 FMR1 CMX- CMX- chrX:147454832 G T NA 0.000646517 G0000031614 V9084269 FMR1 CMX- CMX- chrX:147479861 A C NA 0.000646517 G0000031614 V9084270 FMR1 CMX- CMX- chrX:147480274 A G NA 0.000646517 G0000031614 V9084271 FMR1 CMX- CMX- chrX:147481891 T C NA 0.000646517 G0000031614 V9084272 FMR1 CMX- CMX- chrX:147482603 A G NA 0.000564877 G0000031614 V9084273 FMR1 CMX- CMX- chrX:147482630 A G NA 0.000458631 G0000031614 V9084274 FOXO3 CMX- CMX- chrX:108856108 C T NA 0.000232121 G0000010672 V9084196 FOXO3 CMX- CMX- chr6:109149693 G C NA 0.000344433 G0000010672 V9084197 FOXO3 CMX- CMX- chr6:108853361 T A NA 0.000176018 G0000010672 V9084195 FOXO3 CMX- CMX- chr6:109155789 G T NA 0.000641107 G0000010672 V9084198 FOXO3 CMX- CMX- chr6:108985148- NA CNV FOXO3 NA G0000010672 V1295244 108989762 gain (1 exon) FOXO3 CMX- CMX- chr6:108985507- NA CNV FOXO3 NA G0000010672 V1963522 108989056 gain (1 exon) FOXO3 CMX- CMX- chr6:108984930- NA CNV FOXO3 NA G0000010672 V1963523 108989762 gain (1 exon) FOXP3 CMX- CMX- chrX:48890221- NA CNV FOXP3 NA G0000030750 V1008919 49257528 gain (9 exons) GDF1 CMX- CMX- chr19:18872185- NA CNV GDF1 NA G0000027183 V1625432 19535389 gain (2 exons) GJA4, CMX- CMX- chr1:35000925- NA CNV GJA4 NA GJB3, G0000000643, V1706868 37866010 gain (1 exon), GJB4 CMX- GJB3 G0000000642, (1 exon), CMX- GJB4 G0000000641 (1 exon) GJD3 CMX- CMX- chr17:37952541- NA CNV GJD3 NA G0000025169 V1132225 38532715 gain (1 exon) GPC3 CMX- CMX- chrX:132613906- NA CNV GPC3 NA G0000031486 V1515961 132779666 gain (1 exon) IGF2 CMX- CMX- chr11:2127129- NA CNV IGF2 NA G0000016702 V1454080 2173473 gain (3 exons) IGF2 CMX- CMX- chr11:2110901- NA CNV IGF2 NA G0000016702 V1542559 2173938 gain (3 exons) IGFBPL1 CMX- CMX- chr9:35776310- NA CNV IGFBPL1 NA G0000014341 V1435664 38419649 loss (3 exons) ISG15 CMX- CMX- chr1:940142- NA CNV ISG15 NA G0000000029 V1111642 1016233 gain (2 exons) ISG15 CMX- CMX- chr1:834638- NA CNV ISG15 NA G0000000029 V1884847 1271900 gain (2 exons) KISS1 CMX- CMX- chr1:202729101- NA CNV KISS1 NA G0000002533 V1823995 205013246 gain (2 exons) KISS1R CMX- CMX- chr19:867728- NA CNV KISS1R NA G0000026560 V1469394 945645 gain (2 exons) KISS1R CMX- CMX- chr19:867728- NA CNV KISS1R NA G0000026560 V1974120 1126103 gain (2 exons) KISS1R CMX- CMX- chr19:868013- NA CNV KISS1R NA G0000026560 V1813360 1085518 gain (2 exons) KISS1R CMX- CMX- chr19:866589- NA CNV KISS1R NA G0000026560 V1883755 1232099 gain (2 exons) LOXL4 CMX- CMX- chr10:100013106- NA CNV LOXL4 NA G0000016263 V1039367 100022354 loss (9 exons) LOXL4 CMX- CMX- chr10:100013359- NA CNV LOXL4 NA G0000016263 V1620875 100023161 loss (10 exons) LOXL4 CMX- CMX- chr10:100014360- NA CNV LOXL4 NA G0000016263 V1806767 100020546 loss (6 exons) LOXL4 CMX- CMX- chr10:100014176- NA CNV LOXL4 NA G0000016263 V1954806 100022354 loss (8 exons) LOXL4 CMX- CMX- chr10:100015459- NA CNV LOXL4 NA G0000016263 V1107311 100023313 loss (9 exons) LOXL4 CMX- CMX- chr10:100015459- NA CNV LOXL4 NA G0000016263 V1373344 100023369 loss (9 exons) LOXL4 CMX- CMX- chr10:100015459- NA CNV LOXL4 NA G0000016263 V1073572 100023161 loss (9 exons) LOXL4 CMX- CMX- chr10:100014551- NA CNV LOXL4 NA G0000016263 V1348325 100023161 loss (9 exons) LOXL4 CMX- CMX- chr10:100011910- NA CNV LOXL4 NA G0000016263 V1321127 100023369 loss (11 exons) LOXL4 CMX- CMX- chr10:100013876- NA CNV LOXL4 NA G0000016263 V1323761 103528663 loss (9 exons) LOXL4 CMX- CMX- chr10:100014176- NA CNV LOXL4 NA G0000016263 V1275468 100023161 loss (9 exons) MAP3K2 CMX- CMX- chr2:128093608- NA CNV MAP3K2 NA G0000004205 V1566424 128138545 gain (3 exons) MAP3K2 CMX- CMX- chr2:128098216- NA CNV MAP3K2 NA G0000004205 V1811137 128117112 gain (1 exon) MAP3K2 CMX- CMX- chr2:127520276- NA CNV MAP3K2 NA G0000004205 V1696049 128116794 gain (16 exons) MUC4 CMX- CMX- chr3:195505739 C T Drastic NA G0000006719 V9083756 nonsynonymous MUC4 CMX- CMX- chr3:195505960 G C Drastic NA G0000006719 V9083757 nonsynonymous MUC4 CMX- CMX- chr3:195506089 G A Drastic NA G0000006719 V90837578 nonsynonymous MUC4 CMX- CMX- chr3:195506099 T C Drastic NA G0000006719 V9083759 nonsynonymous MUC4 CMX- CMX- chr3:195505883 T C Drastic NA G0000006719 V9083760 nonsynonymous MUC4 CMX- CMX- chr3:195501149 C T Drastic NA G0000006719 V9083761 nonsynonymous MUC4 CMX- CMX- chr3:195506156 G C Drastic NA G0000006719 V9083762 nonsynonymous MUC4 CMX- CMX- chr3:195505897 G A Drastic NA G0000006719 V9083763 nonsynonymous MUC4 CMX- CMX- chr3:195506146 A G Drastic NA G0000006719 V9083764 nonsynonymous MUC4 CMX- CMX- chr3:195506149 C T Drastic NA G0000006719 V9083765 nonsynonymous MUC4 CMX- CMX- chr3:195506281 A G Drastic NA G0000006719 V9083766 nonsynonymous MUC4 CMX- CMX- chr3:195506291 C T Drastic NA G0000006719 V9083767 nonsynonymous MUC4 CMX- CMX- chr3:195506302 G T Drastic NA G0000006719 V9083768 nonsynonymous MUC4 CMX- CMX- chr3:195506245 C A Drastic NA G0000006719 V9083769 nonsynonymous MUC4 CMX- CMX- chr3:195495916 G C Drastic NA G0000006719 V9083770 nonsynonymous MUC4 CMX- CMX- chr3:195506318 C G Drastic NA G0000006719 V9083771 nonsynonymous MUC4 CMX- CMX- chr3:195506323 G C Drastic NA G0000006719 V9083772 nonsynonymous MUC4 CMX- CMX- chr3:195506339 T G Drastic NA G0000006719 V9083773 nonsynonymous MUC4 CMX- CMX- chr3:195506350 G T Drastic NA G0000006719 V9083774 nonsynonymous MUC4 CMX- CMX- chr3:195506364 G C Drastic NA G0000006719 V9083775 nonsynonymous MUC4 CMX- CMX- chr3:195506185 G A Drastic NA G0000006719 V9083776 nonsynonymous MUC4 CMX- CMX- chr3:195506195 C T Drastic NA G0000006719 V9083777 nonsynonymous MUC4 CMX- CMX- chr3:195506398 G T Drastic NA G0000006719 V9083778 nonsynonymous MUC4 CMX CMX- chr3:195506410 G A Drastic NA G0000006719 V9083779 nonsynonymous MUC4 CMX- CMX- chr3:195506411 C T Drastic NA G0000006719 V9083780 nonsynonymous MUC4 CMX- CMX- chr3:195506446 G T Drastic NA G0000006719 V9083781 nonsynonymous MUC4 CMX- CMX- chr3:195506460 G C Drastic NA G0000006719 V9083782 nonsynonymous MUC4 CMX- CMX- chr3:195506005 A C Drastic NA G0000006719 V9083783 nonsynonymous MUC4 CMX- CMX- chr3:195506521 G A Drastic NA G0000006719 V9083784 nonsynonymous MUC4 CMX- CMX- chr3:195506533 C A Drastic NA G0000006719 V9083785 nonsynonymous MUC4 CMX- CMX- chr3:195506542 G T Drastic NA G0000006719 V9083786 nonsynonymous MUC4 CMX- CMX- chr3:195505788 G C Drastic NA G0000006719 V9083787 nonsynonymous MUC4 CMX- CMX- chr3:195506558 G C Drastic NA G0000006719 V9083788 nonsynonymous MUC4 CMX- CMX- chr3:195506590 G A Drastic NA G0000006719 V9083789 nonsynonymous MUC4 CMX- CMX- chr3:195506597 G A Drastic NA G0000006719 V9083790 nonsynonymous MUC4 CMX- CMX- chr3:195505906 G A Drastic NA G0000006719 V9083791 nonsynonymous MUC4 CMX- CMX- chr3:195506626 G A Drastic NA G0000006719 V9083792 nonsynonymous MUC4 CMX- CMX- chr3:195506627 T G Drastic NA G0000006719 V9083793 nonsynonymous MUC4 CMX- CMX- chr3:195506740 G C Drastic NA G0000006719 V9083794 nonsynonymous MUC4 CMX- CMX- chr3:195506746 G A Drastic NA G0000006719 V9083795 nonsynonymous MUC4 CMX- CMX- chr3:195506494 G T Drastic NA G0000006719 V9083796 nonsynonymous MUC4 CMX- CMX- chr3:195506750 G C Drastic NA G0000006719 V9083797 nonsynonymous MUC4 CMX- CMX- chr3:195506752 C T Drastic NA G0000006719 V9083798 nonsynonymous MUC4 CMX- CMX- chr3:195506753 G C Drastic NA G0000006719 V9083799 nonsynonymous MUC4 CMX- CMX- chr3:195506809 G T Drastic NA G0000006719 V9083800 nonsynonymous MUC4 CMX- CMX- chr3:195506914 G A Drastic NA G0000006719 V9083801 nonsynonym MUC4 CMX- CMX- chr3:195506917 A C Drastic NA G0000006719 V9083802 nonsynonym MUC4 CMX- CMX- chr3:195506933 G A Drastic NA G0000006719 V9083803 nonsynonymous MUC4 CMX- CMX- chr3:195506940 G C Drastic NA G0000006719 V9083804 nonsynonymous MUC4 CMX- CMX- chr3:195506953 G A Drastic NA G0000006719 V9083805 nonsynonymous MUC4 CMX- CMX- chr3:195506965 T C Drastic NA G0000006719 V9083806 nonsynonymous MUC4 CMX- CMX- chr3:195506966 C T Drastic NA G0000006719 V9083807 nonsynonymous MUC4 CMX- CMX- chr3:195506975 G C Drastic NA G0000006719 V9083808 nonsynonymous MUC4 CMX- CMX- chr3:195506747 C T Drastic NA G0000006719 V9083809 nonsynonymous MUC4 CMX- CMX- chr3:195506986 G A Drastic NA G0000006719 V9083810 nonsynonymous MUC4 CMX- CMX- chr3:195506987 T C Drastic NA G0000006719 V9083811 nonsynonymous MUC4 CMX- CMX- chr3:195506990 C G Drastic NA G0000006719 V9083812 nonsynonymous MUC4 CMX- CMX- chr3:195507010 A G Drastic NA G0000006719 V9083813 nonsynonymous MUC4 CMX- CMX- chr3:195507059 T C Drastic NA G0000006719 V9083814 nonsynonymous MUC4 CMX- CMX- chr3:195507062 C T Drastic NA G0000006719 V9083815 nonsynonymous MUC4 CMX- CMX chr3:195506378 C A Drastic NA G0000006719 V9083816 nonsynonymous MUC4 CMX- CMX- chr3:195507083 T C Drastic NA G0000006719 V9083817 nonsynonymous MUC4 CMX- CMX- chr3:195507086 C G Drastic NA G0000006719 V9083818 nonsynonymous MUC4 CMX- CMX- chr3:195507107 C T Drastic NA G0000006719 V9083819 nonsynonymous MUC4 CMX- CMX- chr3:195507166 A G Drastic NA G0000006719 V9083820 nonsynonymous MUC4 CMX- CMX- chr3:195507203 T G Drastic NA G0000006719 V9083821 nonsynonymous MUC4 CMX- CMX- chr3:195507226 A G Drastic NA G0000006719 V9083822 nonsynonymous MUC4 CMX- CMX- chr3:195507228 G C Drastic NA G0000006719 V9083823 nonsynonymous MUC4 CMX- CMX- chr3:195507236 T C Drastic NA G0000006719 V9083824 nonsynonymous MUC4 CMX- CMX- chr3:195507242 C A Drastic NA G0000006719 V9083825 nonsynonymous MUC4 CMX- CMX- chr3:195507251 G T Drastic NA G0000006719 V9083826 nonsynonymous MUC4 CMX- CMX- chr3:195507262 T G Drastic NA G0000006719 V9083827 nonsynonymous MUC4 CMX- CMX- chr3:195507316 G A Drastic NA G0000006719 V9083828 nonsynonymous MUC4 CMX- CMX- chr3:195507323 T C Drastic NA G0000006719 V9083829 nonsynonymous MUC4 CMX- CMX- chr3:195507324 G C Drastic NA G0000006719 V9083830 nonsynonymous MUC4 CMX- CMX- chr3:195507365 G A Drastic NA G0000006719 V9083831 nonsynonymous MUC4 CMX- CMX- chr3:195507379 G C Drastic NA G0000006719 V9083832 nonsynonymous MUC4 CMX- CMX- chr3:195507385 G A Drastic NA G0000006719 V9083833 nonsynonymous MUC4 CMX- CMX- chr3:195507397 T C Drastic NA G0000006719 V9083834 nonsynonymous MUC4 CMX- CMX- chr3:195507398 C T Drastic NA G0000006719 V9083835 nonsynonymous MUC4 CMX- CMX- chr3:195507406 G A Drastic NA G0000006719 V9083836 nonsynonymous MUC4 CMX- CMX- chr3:195507412 C G Drastic NA G0000006719 V9083837 nonsynonymous MUC4 CMX- CMX- chr3:195507422 C G Drastic NA G0000006719 V9083838 nonsynonymous MUC4 CMX- CMX- chr3:195507428 T A Drastic NA G0000006719 V9083839 nonsynonymous MUC4 CMX- CMX- chr3:195507433 G A Drastic NA G0000006719 V9083840 nonsynonymous MUC4 CMX- CMX- chr3:195507434 C A Drastic NA G0000006719 V9083841 nonsynonymous MUC4 CMX- CMX- chr3:195507443 T G Drastic NA G0000006719 V9083842 nonsynonymous MUC4 CMX- CMX- chr3:195507445 T A Drastic NA G0000006719 V9083843 nonsynonymous MUC4 CMX- CMX- chr3:195507446 C T Drastic NA G0000006719 V9083844 nonsynonymous MUC4 CMX- CMX- chr3:195507461 G A Drastic NA G0000006719 V9083845 nonsynonymous MUC4 CMX- CMX- chr3:195507475 G C Drastic NA G0000006719 V9083846 nonsynonymous MUC4 CMX- CMX- chr3:195507491 C T Drastic NA G0000006719 V9083847 nonsynonymous MUC4 CMX- CMX- chr3:195507494 C T Drastic NA G0000006719 V9083848 nonsynonymous MUC4 CMX- CMX- chr3:195507502 A G Drastic NA G0000006719 V9083849 nonsynonymous MUC4 CMX- CMX- chr3:195507504 C G Drastic NA G0000006719 V9083850 nonsynonymous MUC4 CMX- CMX- chr3:195507605 G A Drastic NA G0000006719 V9083851 nonsynonymous MUC4 CMX- CMX- chr3:195507614 C G Drastic NA G0000006719 V9083852 nonsynonymous MUC4 CMX- CMX- chr3:195507620 T A Drastic NA G0000006719 V9083853 nonsynonymous MUC4 CMX- CMX- chr3:195507625 G A Drastic NA G0000006719 V9083854 nonsynonymous MUC4 CMX- CMX- chr3:195507635 T G Drastic NA G0000006719 V9083855 nonsynonymous MUC4 CMX- CMX- chr3:195507077 G A Drastic NA G0000006719 V9083856 nonsynonymous MUC4 CMX- CMX- chr3:195507694 A G Drastic NA G0000006719 V9083857 nonsynonymous MUC4 CMX- CMX- chr3:195507731 G A Drastic NA G0000006719 V9083858 nonsynonymous MUC4 CMX- CMX- chr3:195507779 C T Drastic NA G0000006719 V9083859 nonsynonymous MUC4 CMX- CMX- chr3:195507790 G A Drastic NA G0000006719 V9083860 nonsynonymous MUC4 CMX- CMX- chr3:195507827 G A Drastic NA G0000006719 V9083861 nonsynonymous MUC4 CMX- CMX- chr3:195474159 G A Drastic NA G0000006719 V9083862 nonsynonymous MUC4 CMX- CMX- chr3:195477786 C T Drastic NA G0000006719 V9083863 nonsynonymous MUC4 CMX- CMX- chr3:195489009 C A Drastic NA G0000006719 V9083864 nonsynonymous MUC4 CMX- CMX- chr3:195508019 G C Drastic NA G0000006719 V9083865 nonsynonymous MUC4 CMX- CMX- chr3:195508021 C T Drastic NA G0000006719 V9083866 nonsynonymous MUC4 CMX- CMX- chr3:195508069 T C Drastic NA G0000006719 V9083867 nonsynonymous MUC4 CMX- CMX- chr3:195508070 C T Drastic NA G0000006719 V9083868 nonsynonymous MUC4 CMX- CMX- chr3:195508091 T C Drastic NA G0000006719 V9083869 nonsynonymous MUC4 CMX- CMX- chr3:195505886 C G Drastic NA G0000006719 V9083870 nonsynonymous MUC4 CMX- CMX- chr3:195508115 T G Drastic NA G0000006719 V9083871 nonsynonymous MUC4 CMX- CMX- chr3:195508127 G C Drastic NA G0000006719 V9083872 nonsynonymous MUC4 CMX- CMX- chr3:195505907 T G Drastic NA G0000006719 V9083873 nonsynonymous MUC4 CMX- CMX- chr3:195505930 C G Drastic NA G0000006719 V9083874 nonsynonymous MUC4 CMX- CMX- chr3:195505955 C T Drastic NA G0000006719 V9083875 nonsynonymous MUC4 CMX- CMX- chr3:195508336 C T Drastic NA G0000006719 V9083876 nonsynonymous MUC4 CMX- CMX- chr3:195505979 T C Drastic NA G0000006719 V9083877 nonsynonymous MUC4 CMX- CMX- chr3:195508451 G T Drastic NA G0000006719 V9083878 nonsynonymous MUC4 CMX- CMX- chr3:195508453 C T Drastic NA G0000006719 V9083879 nonsynonymous MUC4 CMX- CMX- chr3:195508475 C T Drastic NA G0000006719 V9083880 nonsynonymous MUC4 CMX- CMX- chr3:195508478 G C Drastic NA G0000006719 V9083881 nonsynonymous MUC4 CMX- CMX- chr3:195508500 G C Drastic NA G0000006719 V9083882 nonsynonymous MUC4 CMX- CMX- chr3:195508501 T C Drastic NA G0000006719 V9083883 nonsynonymous MUC4 CMX- CMX- chr3:195508502 C T Drastic NA G0000006719 V9083884 nonsynonymous MUC4 CMX- CMX- chr3:195508523 C T Drastic NA G0000006719 V9083885 nonsynonymous MUC4 CMX- CMX- chr3:195508536 G C Drastic NA G0000006719 V9083886 nonsynonymous MUC4 CMX- CMX- chr3:195508667 T C Drastic NA G0000006719 V9083887 nonsynonymous MUC4 CMX- CMX- chr3:195508668 G C Drastic NA G0000006719 V9083888 nonsynonymous MUC4 CMX- CMX- chr3:195508702 G A Drastic NA G0000006719 V9083889 nonsynonymous MUC4 CMX- CMX- chr3:195506311 G C Drastic NA G0000006719 V9083890 nonsynonymous MUC4 CMX- CMX- chr3:195506315 T C Drastic NA G0000006719 V9083891 nonsynonymous MUC4 CMX- CMX- chr3:195508787 G T Drastic NA G0000006719 V9083892 nonsynonymous MUC4 CMX- CMX- chr3:195508789 C T Drastic NA G0000006719 V9083893 nonsynonymous MUC4 CMX- CMX- chr3:195509092 C T Drastic NA G0000006719 V9083894 nonsynonymous MUC4 CMX- CMX- chr3:195509093 G A Drastic NA G0000006719 V9083895 nonsynonymous MUC4 CMX- CMX- chr3:195509099 T C Drastic NA G0000006719 V9083896 nonsynonymous MUC4 CMX- CMX- chr3:195509102 G C Drastic NA G0000006719 V9083897 nonsynonymous MUC4 CMX- CMX- chr3:195506389 C T Drastic NA G0000006719 V9083898 nonsynonymous MUC4 CMX- CMX- chr3:195509212 G A Drastic NA G0000006719 V9083899 nonsynonymous MUC4 CMX- CMX- chr3:195509287 T G Drastic NA G0000006719 V9083900 nonsynonymous MUC4 CMX- CMX- chr3:195509353 G A Drastic NA G0000006719 V9083901 nonsynonymous MUC4 CMX- CMX- chr3:195509354 C T Drastic NA G0000006719 V9083902 nonsynonymous MUC4 CMX- CMX- chr3:195509363 G T Drastic NA G0000006719 V9083903 nonsynonymous MUC4 CMX- CMX- chr3:195509365 C T Drastic NA G0000006719 V9083904 nonsynonymous MUC4 CMX- CMX- chr3:195509374 T G Drastic NA G0000006719 V9083905 nonsynonymous MUC4 CMX CMX- chr3:195509378 G C Drastic NA G0000006719 V9083906 nonsynonymous MUC4 CMX- CMX- chr3:195509423 G A Drastic NA G0000006719 V9083907 nonsynonymous MUC4 CMX- CMX- chr3:195506554 G A Drastic NA G0000006719 V9083908 nonsynonymous MUC4 CMX- CMX- chr3:195509563 A T Drastic NA G0000006719 V9083909 nonsynonymous MUC4 CMX- CMX- chr3:195509573 A G Drastic NA G0000006719 V9083910 nonsynonymous MUC4 CMX- CMX- chr3:195509606 C T Drastic NA G0000006719 V9083911 nonsynonymous MUC4 CMX- CMX- chr3:195506617 G A Drastic NA G0000006719 V9083912 nonsynonymous MUC4 CMX- CMX- chr3:195509627 T C Drastic NA G0000006719 V9083913 nonsynonymous MUC4 CMX- CMX- chr3:195509651 G A Drastic NA G0000006719 V9083914 nonsynonymous MUC4 CMX- CMX- chr3:195509756 G C Drastic NA G0000006719 V9083915 nonsynonymous MUC4 CMX- CMX- chr3:195509795 C T Drastic NA G0000006719 V9083916 nonsynonymous MUC4 CMX- CMX- chr3:195509861 A G Drastic NA G0000006719 V9083917 nonsynonymous MUC4 CMX- CMX- chr3:195509879 A G Drastic NA G0000006719 V9083918 nonsynonymous MUC4 CMX- CMX- chr3:195509918 G C Drastic NA G0000006719 V9083919 nonsynonymous MUC4 CMX- CMX- chr3:195509939 G T Drastic NA G0000006719 V9083920 nonsynonymous MUC4 CMX- CMX- chr3:195509941 A C Drastic NA G0000006719 V9083921 nonsynonymous MUC4 CMX- CMX- chr3:195509954 G C Drastic NA G0000006719 V9083922 nonsynonymous MUC4 CMX- CMX- chr3:195509957 A G Drastic NA G0000006719 V9083923 nonsynonymous MUC4 CMX- CMX- chr3:195509974 A G Drastic NA G0000006719 V9083924 nonsynonymous MUC4 CMX- CMX- chr3:195510068 T A Drastic NA G0000006719 V9083925 nonsynonymous MUC4 CMX- CMX- chr3:195510083 G T Drastic NA G0000006719 V9083926 nonsynonymous MUC4 CMX- CMX- chr3:195510146 G C Drastic NA G0000006719 V9083927 nonsynonymous MUC4 CMX- CMX- chr3:195510194 G C Drastic NA G0000006719 V9083928 nonsynonymous MUC4 CMX- CMX- chr3:195510590 C G Drastic NA G0000006719 V9083929 nonsynonymous MUC4 CMX- CMX- chr3:195506983 G A Drastic NA G0000006719 V9083930 nonsynonymous MUC4 CMX- CMX- chr3:195510655 T G Drastic NA G0000006719 V9083931 nonsynonymous MUC4 CMX- CMX- chr3:195510659 T C Drastic NA G0000006719 V9083932 nonsynonymous MUC4 CMX- CMX- chr3:195510662 C T Drastic NA G0000006719 V9083933 nonsynonymous MUC4 CMX- CMX- chr3:195510683 T C Drastic NA G0000006719 V9083934 nonsynonymous MUC4 CMX- CMX- chr3:195510686 C G Drastic NA G0000006719 V9083935 nonsynonymous MUC4 CMX- CMX- chr3:195510697 G A Drastic NA G0000006719 V9083936 nonsynonymous MUC4 CMX- CMX- chr3:195510706 G A Drastic NA G0000006719 V9083937 nonsynonymous MUC4 CMX- CMX- chr3:195510707 T G Drastic NA G0000006719 V9083938 nonsynonymous MUC4 CMX- CMX- chr3:195510709 C T Drastic NA G0000006719 V9083939 nonsynonymous MUC4 CMX- CMX- chr3:195510718 G T Drastic NA G0000006719 V9083940 nonsynonymous MUC4 CMX- CMX- chr3:195510745 G A Drastic NA G0000006719 V9083941 nonsynonymous MUC4 CMX- CMX- chr3:195510749 C A Drastic NA G0000006719 V9083942 nonsynonymous MUC4 CMX- CMX chr3:195510766 G T Drastic NA G0000006719 V9083943 nonsynonymous MUC4 CMX- CMX chr3:195510767 G A Drastic NA G0000006719 V9083944 nonsynonymous MUC4 CMX- CMX- chr3:195510773 A G Drastic NA G0000006719 V9083945 nonsynonymous MUC4 CMX- CMX- chr3:195510827 C T Drastic NA G0000006719 V9083946 nonsynonymous MUC4 CMX- CMX- chr3:195510896 G A Drastic NA G0000006719 V9083947 nonsynonymous MUC4 CMX- CMX- chr3:195510899 T C Drastic NA G0000006719 V9083948 nonsynonymous MUC4 CMX- CMX- chr3:195510910 G T Drastic NA G0000006719 V9083949 nonsynonymous MUC4 CMX- CMX- chr3:195510943 G T Drastic NA G0000006719 V9083950 nonsynonymous MUC4 CMX- CMX- chr3:195511013 G A Drastic NA G0000006719 V9083951 nonsynonymous MUC4 CMX- CMX- chr3:195511019 T C Drastic NA G0000006719 V9083952 nonsynonymous MUC4 CMX- CMX- chr3:195511043 T C Drastic NA G0000006719 V9083953 nonsynonymous MUC4 CMX- CMX- chr3:195511051 C A Drastic NA G0000006719 V9083954 nonsynonymous MUC4 CMX- CMX- chr3:195511070 C G Drastic NA G0000006719 V9083955 nonsynonymous MUC4 CMX- CMX- chr3:195511076 T A Drastic NA G0000006719 V9083956 nonsynonymous MUC4 CMX- CMX- chr3:195511102 G A Drastic NA G0000006719 V9083957 nonsynonymous MUC4 CMX- CMX- chr3:195511142 T C Drastic NA G0000006719 V9083958 nonsynonymous MUC4 CMX- CMX- chr3:195511156 C G Drastic NA G0000006719 V9083959 nonsynonymous MUC4 CMX- CMX- chr3:195511186 A G Drastic NA G0000006719 V9083960 nonsynonymous MUC4 CMX- CMX- chr3:195511190 C T Drastic NA G0000006719 V9083961 nonsynonymous MUC4 CMX- CMX- chr3:195511204 T G Drastic NA G0000006719 V9083962 nonsynonymous MUC4 CMX- CMX- chr3:195511211 C T Drastic NA G0000006719 V9083963 nonsynonymous MUC4 CMX- CMX- chr3:195511214 G C Drastic NA G0000006719 V9083964 nonsynonymous MUC4 CMX- CMX- chr3:195511268 T A Drastic NA G0000006719 V9083965 nonsynonymous MUC4 CMX- CMX- chr3:195511273 G A Drastic NA G0000006719 V9083966 nonsynonymous MUC4 CMX- CMX- chr3:195511285 T C Drastic NA G0000006719 V9083967 nonsynonymous MUC4 CMX- CMX- chr3:195511286 T C Drastic NA G0000006719 V9083968 nonsynonymous MUC4 CMX- CMX- chr3:195511331 A G Drastic NA G0000006719 V9083969 nonsynonymous MUC4 CMX- CMX- chr3:195511336 G C Drastic NA G0000006719 V9083970 nonsynonymous MUC4 CMX- CMX- chr3:195511358 C G Drastic NA G0000006719 V9083971 nonsynonymous MUC4 CMX- CMX- chr3:195511390 C G Drastic NA G0000006719 V9083972 nonsynonymous MUC4 CMX- CMX- chr3:195511396 G A Drastic NA G0000006719 V9083973 nonsynonymous MUC4 CMX- CMX- chr3:195511403 C T Drastic NA G0000006719 V9083974 nonsynonymous MUC4 CMX- CMX- chr3:195511412 T A Drastic NA G0000006719 V9083975 nonsynonymous MUC4 CMX- CMX- chr3:195511438 G T Drastic NA G0000006719 V9083976 nonsynonymous MUC4 CMX- CMX- chr3:195507683 C T Drastic NA G0000006719 V9083977 nonsynonymous MUC4 CMX- CMX- chr3:195511454 C G Drastic NA G0000006719 V9083978 nonsynonymous MUC4 CMX- CMX- chr3:195511460 T A Drastic NA G0000006719 V9083979 nonsynonymous MUC4 CMX- CMX- chr3:195511465 G A Drastic NA G0000006719 V9083980 nonsynonymous MUC4 CMX- CMX- chr3:195511474 A G Drastic NA G0000006719 V9083981 nonsynonymous MUC4 CMX- CMX- chr3:195511486 G T Drastic NA G0000006719 V9083982 nonsynonymous MUC4 CMX- CMX- chr3:195507925 C T Drastic NA G0000006719 V9083983 nonsynonymous MUC4 CMX- CMX- chr3:195508009 G A Drastic NA G0000006719 V9083984 nonsynonymous MUC4 CMX- CMX- chr3:195508010 C A Drastic NA G0000006719 V9083985 nonsynonymous MUC4 CMX- CMX- chr3:195511513 G A Drastic NA G0000006719 V9083986 nonsynonymous MUC4 CMX- CMX- chr3:195511525 T C Drastic NA G0000006719 V9083987 nonsynonymous MUC4 CMX- CMX- chr3:195511526 C T Drastic NA G0000006719 V9083988 nonsynonymous MUC4 CMX- CMX- chr3:195511534 T G Drastic NA G0000006719 V9083989 nonsynonymous MUC4 CMX- CMX- chr3:195511547 C T Drastic NA G0000006719 V9083990 nonsynonymous MUC4 CMX- CMX- chr3:195508108 G A Drastic NA G0000006719 V9083991 nonsynonymous MUC4 CMX- CMX- chr3:195511690 G C Drastic NA G0000006719 V9083992 nonsynonymous MUC4 CMX- CMX- chr3:195511705 G A Drastic NA G0000006719 V9083993 nonsynonymous MUC4 CMX- CMX- chr3:195508175 G C Drastic NA G0000006719 V9083994 nonsynonymous MUC4 CMX- CMX- chr3:195508178 G C Drastic NA G0000006719 V9083995 nonsynonymous MUC4 CMX- CMX- chr3:195508238 C G Drastic NA G0000006719 V9083996 nonsynonymous MUC4 CMX- CMX- chr3:195511822 G T Drastic NA G0000006719 V9083997 nonsynonymous MUC4 CMX- CMX- chr3:195508402 G T Drastic NA G0000006719 V9083998 nonsynonymous MUC4 CMX- CMX- chr3:195511870 G A Drastic NA G0000006719 V9083999 nonsynonymous MUC4 CMX- CMX- chr3:195511877 G A Drastic NA G0000006719 V9084000 nonsynonymous MUC4 CMX- CMX- chr3:195511918 G T Drastic NA G0000006719 V9084001 nonsynonymous MUC4 CMX- CMX- chr3:195511925 A G Drastic NA G0000006719 V9084002 nonsynonymous MUC4 CMX- CMX- chr3:195511937 C T Drastic NA G0000006719 V9084003 nonsynonymous MUC4 CMX- CMX- chr3:195512042 T C Drastic NA G0000006719 V9084004 nonsynonymous MUC4 CMX- CMX- chr3:195512107 T A Drastic NA G0000006719 V9084005 nonsynonymous MUC4 CMX- CMX- chr3:195512117 C G Drastic NA G0000006719 V9084006 nonsynonymous MUC4 CMX- CMX- chr3:195512195 C T Drastic NA G0000006719 V9084007 nonsynonymous MUC4 CMX- CMX- chr3:195512206 A G Drastic NA G0000006719 V9084008 nonsynonymous MUC4 CMX- CMX- chr3:195512212 G T Drastic NA G0000006719 V9084009 nonsynonymous MUC4 CMX- CMX- chr3:195512242 G A Drastic NA G0000006719 V9084010 nonsynonymous MUC4 CMX- CMX- chr3:195508774 G T Drastic NA G0000006719 V9084011 nonsynonymous MUC4 CMX- CMX- chr3:195508786 A G Drastic NA G0000006719 V9084012 nonsynonymous MUC4 CMX- CMX- chr3:195512267 T C Drastic NA G0000006719 V9084013 nonsynonymous MUC4 CMX- CMX- chr3:195512270 C G Drastic NA G0000006719 V9084014 nonsynonymous MUC4 CMX- CMX- chr3:195512287 G A Drastic NA G0000006719 V9084015 nonsynonymous MUC4 CMX- CMX- chr3:195512302 G A Drastic NA G0000006719 V9084016 nonsynonymous MUC4 CMX- CMX- chr3:195512567 G A Drastic NA G0000006719 V9084017 nonsynonymous MUC4 CMX- CMX- chr3:195512597 G A Drastic NA G0000006719 V9084018 nonsynonymous MUC4 CMX- CMX- chr3:195509170 A G Drastic NA G0000006719 V9084019 nonsynonymous MUC4 CMX- CMX- chr3:195512606 G C Drastic NA G0000006719 V9084020 nonsynonymous MUC4 CMX- CMX- chr3:195512665 G A Drastic NA G0000006719 V9084021 nonsynonymous MUC4 CMX- CMX- chr3:195512686 G T Drastic NA G0000006719 V9084022 nonsynonymous MUC4 CMX- CMX- chr3:195512693 A G Drastic NA G0000006719 V9084023 nonsynonymous MUC4 CMX- CMX- chr3:195512767 T G Drastic NA G0000006719 V9084024 nonsynonymous MUC4 CMX- CMX- chr3:195512768 T A Drastic NA G0000006719 V9084025 nonsynonymous MUC4 CMX- CMX- chr3:195513136 G C Drastic NA G0000006719 V9084026 nonsynonymous MUC4 CMX- CMX- chr3:195513154 G T Drastic NA G0000006719 V9084027 nonsynonymous MUC4 CMX- CMX- chr3:195513155 T C Drastic NA G0000006719 V9084028 nonsynonymous MUC4 CMX- CMX- chr3:195509476 A G Drastic NA G0000006719 V9084029 nonsynonymous MUC4 CMX- CMX- chr3:195513203 C T Drastic NA G0000006719 V9084030 nonsynonymous MUC4 CMX- CMX- chr3:195513214 A G Drastic NA G0000006719 V9084031 nonsynonymous MUC4 CMX- CMX- chr3:195513364 C T Drastic NA G0000006719 V9084032 nonsynonymous MUC4 CMX- CMX- chr3:195509614 G A Drastic NA G0000006719 V9084033 nonsynonymous MUC4 CMX- CMX- chr3:195513383 T A Drastic NA G0000006719 V9084034 nonsynonymous MUC4 CMX- CMX- chr3:195513394 A T Drastic NA G0000006719 V9084035 nonsynonymous MUC4 CMX- CMX- chr3:195513395 G T Drastic NA G0000006719 V9084036 nonsynonymous MUC4 CMX- CMX- chr3:195513397 C T Drastic NA G0000006719 V9084037 nonsynonymous MUC4 CMX- CMX- chr3:195513398 C T Drastic NA G0000006719 V9084038 nonsynonymous MUC4 CMX- CMX- chr3:195513413 G A Drastic NA G0000006719 V9084039 nonsynonymous MUC4 CMX- CMX- chr3:195513433 G A Drastic NA G0000006719 V9084040 nonsynonymous MUC4 CMX- CMX- chr3:195513442 G T Drastic NA G0000006719 V9084041 nonsynonymous MUC4 CMX- CMX- chr3:195513445 C T Drastic NA G0000006719 V9084042 nonsynonymous MUC4 CMX- CMX- chr3:195513461 G A Drastic NA G0000006719 V9084043 nonsynonymous MUC4 CMX- CMX- chr3:195513491 G T Drastic NA G0000006719 V9084044 nonsynonymous MUC4 CMX- CMX- chr3:195513502 T G Drastic NA G0000006719 V9084045 nonsynonymous MUC4 CMX- CMX- chr3:195513515 C T Drastic NA G0000006719 V9084046 nonsynonymous MUC4 CMX- CMX- chr3:195513598 G A Drastic NA G0000006719 V9084047 nonsynonymous MUC4 CMX- CMX- chr3:195513667 T G Drastic NA G0000006719 V9084048 nonsynonymous MUC4 CMX- CMX- chr3:195513743 G T Drastic NA G0000006719 V9084049 nonsynonymous MUC4 CMX- CMX- chr3:195513779 C T Drastic NA G0000006719 V9084050 nonsynonymous MUC4 CMX- CMX- chr3:195510649 G A Drastic NA G0000006719 V9084051 nonsynonymous MUC4 CMX- CMX- chr3:195513991 G A Drastic NA G0000006719 V9084052 nonsynonymous MUC4 CMX- CMX- chr3:195514109 C A Drastic NA G0000006719 V9084053 nonsynonymous MUC4 CMX- CMX- chr3:195514144 T C Drastic NA G0000006719 V9084054 nonsynonymous MUC4 CMX- CMX- chr3:195514324 G A Drastic NA G0000006719 V9084055 nonsynonymous MUC4 CMX- CMX- chr3:195514379 T C Drastic NA G0000006719 V9084056 nonsynonymous MUC4 CMX- CMX- chr3:195514403 C T Drastic NA G0000006719 V9084057 nonsynonymous MUC4 CMX- CMX- chr3:195514643 T G Drastic NA G0000006719 V9084058 nonsynonymous MUC4 CMX- CMX- chr3:195514645 T C Drastic NA G0000006719 V9084059 nonsynonymous MUC4 CMX- CMX- chr3:195514646 C T Drastic NA G0000006719 V9084060 nonsynonymous MUC4 CMX- CMX- chr3:195514654 A G Drastic NA G0000006719 V9084061 nonsynonymous MUC4 CMX- CMX- chr3:195514661 A G Drastic NA G0000006719 V9084062 nonsynonymous MUC4 CMX- CMX- chr3:195514718 G C Drastic NA G0000006719 V9084063 nonsynonymous MUC4 CMX- CMX- chr3:195514729 G A Drastic NA G0000006719 V9084064 nonsynonymous MUC4 CMX- CMX- chr3:195514733 C A Drastic NA G0000006719 V9084065 nonsynonymous MUC4 CMX- CMX- chr3:195514741 A C Drastic NA G0000006719 V9084066 nonsynonymous MUC4 CMX- CMX- chr3:195514757 A G Drastic NA G0000006719 V9084067 nonsynonymous MUC4 CMX- CMX- chr3:195514805 G A Drastic NA G0000006719 V9084068 nonsynonymous MUC4 CMX- CMX- chr3:195514811 C T Drastic NA G0000006719 V9084069 nonsynonymous MUC4 CMX- CMX- chr3:195514812 G C Drastic NA G0000006719 V9084070 nonsynonymous MUC4 CMX- CMX- chr3:195514825 G A Drastic NA G0000006719 V9084071 nonsynonymous MUC4 CMX- CMX- chr3:195514846 A G Drastic NA G0000006719 V9084072 nonsynonymous MUC4 CMX- CMX- chr3:195514859 C T Drastic NA G0000006719 V9084073 nonsynonymous MUC4 CMX- CMX- chr3:195514862 G C Drastic NA G0000006719 V9084074 nonsynonymous MUC4 CMX- CMX- chr3:195514873 G A Drastic NA G0000006719 V9084075 nonsynonymous MUC4 CMX- CMX- chr3:195514882 G A Drastic NA G0000006719 V9084076 nonsynonymous MUC4 CMX- CMX- chr3:195514930 A G Drastic NA G0000006719 V9084077 nonsynonymous MUC4 CMX- CMX- chr3:195514948 G A Drastic NA G0000006719 V9084078 nonsynonymous MUC4 CMX- CMX- chr3:195514969 G A Drastic NA G0000006719 V9084079 nonsynonymous MUC4 CMX- CMX- chr3:195515003 T C Drastic NA G0000006719 V9084080 nonsynonymous MUC4 CMX- CMX- chr3:195515008 C G Drastic NA G0000006719 V9084081 nonsynonymous MUC4 CMX- CMX- chr3:195515038 G A Drastic NA G0000006719 V9084082 nonsynonymous MUC4 CMX- CMX- chr3:195515045 A G Drastic NA G0000006719 V9084083 nonsynonymous MUC4 CMX- CMX- chr3:195515122 G C Drastic NA G0000006719 V9084084 nonsynonymous MUC4 CMX- CMX- chr3:195515134 G T Drastic NA G0000006719 V9084085 nonsynonymous MUC4 CMX- CMX- chr3:195515141 A G Drastic NA G0000006719 V9084086 nonsynonymous MUC4 CMX- CMX- chr3:195515194 G C Drastic NA G0000006719 V9084087 nonsynonymous MUC4 CMX- CMX- chr3:195515387 T C Drastic NA G0000006719 V9084088 nonsynonymous MUC4 CMX- CMX- chr3:195515411 G T Drastic NA G0000006719 V9084089 nonsynonymous MUC4 CMX- CMX- chr3:195515413 C T Drastic NA G0000006719 V9084090 nonsynonymous MUC4 CMX- CMX- chr3:195515449 A T Drastic NA G0000006719 V9084091 nonsynonymous MUC4 CMX- CMX- chr3:195515459 C T Drastic NA G0000006719 V9084092 nonsynonymous MUC4 CMX- CMX- chr3:195538901 C T Start codon NA G0000006719 V9084093 gained MUC4 CMX- CMX- chr3:195512246 T C Drastic NA G0000006719 V9084094 nonsynonymous MUC4 CMX- CMX- chr3:195511556 T A Drastic NA G0000006719 V9084095 nonsynonymous MUC4 CMX- CMX- chr3:195512603 T C Drastic NA G0000006719 V9084096 nonsynonymous MUC4 CMX- CMX- chr3:195513173 G A Drastic NA G0000006719 V9084097 nonsynonymous MUC4 CMX- CMX- chr3:195511451 T C Drastic NA G0000006719 V9084098 nonsynonymous MUC4 CMX- CMX- chr3:195511781 G A Drastic NA G0000006719 V9084099 nonsynonymous MUC4 CMX- CMX- chr3:195511499 C T Drastic NA G0000006719 V9084100 nonsynonymous MUC4 CMX- CMX- chr3:195513365 G A Drastic NA G0000006719 V9084101 nonsynonymous MUC4 CMX- CMX- chr3:195511780 G A Drastic NA G0000006719 V9084102 nonsynonymous MUC4 CMX- CMX- chr3:195513826 G A Drastic NA G0000006719 V9084103 nonsynonymous MUC4 CMX- CMX- chr3:195512245 T C Drastic NA G0000006719 V9084104 nonsynonymous MUC4 CMX- CMX- chr3:195511500 G C Drastic NA G0000006719 V9084105 nonsynonymous MUC4 CMX CMX- chr3:195511502 G C Drastic NA G0000006719 V9084106 nonsynonymous MUC4 CMX- CMX- chr3:195511859 T G Drastic NA G0000006719 V9084107 nonsynonymous MUC4 CMX- CMX- chr3:195511783 A G Drastic NA G0000006719 V9084108 nonsynonymous MUC4 CMX- CMX- chr3:195512373 G GGAT Codon NA G0000006719 SV00002 change and codon insertion MUC4 CMX- CMX- chr3:195518112 T TGTC Codon NA G0000006719 SV00003 TCCT change and GCGT codon AACA insertion MUC4 CMX- CMX- chr3:195464985 CNV NA Splice NA G0000006719 SV00004 duplication acceptor variant MUC4 CMX- CMX- chr3:195507809 CNV NA Nonsynonymous NA G0000006719 SV00005 deletion and coding sequence MUC4 CMX- CMX- chr3:195508499 CNV NA Frameshift NA G0000006719 SV00006 duplication MUC4 CMX- CMX- chr3:195499847 A G NA 6.75E−05 G0000006719 V9084187 MUC4 CMX- CMX- chr3:195500367 A G NA 0.000532509 G0000006719 V9084188 MUC4 CMX- CMX- chr3:195506750 G C NA 0.000425548 G0000006719 V9084191 MUC4 CMX- CMX- chr3:195506760 T A NA 7.68E−05 G0000006719 V9084192 MUC4 CMX- CMX- chr3:195506195 C T NA 8.00E−05 G0000006719 V9084189 MUC4 CMX- CMX- chr3:195506746 G A NA 0.000150373 G0000006719 V9084190 NLRP11 CMX- CMX- chr19:56323263 G A Drastic NA G0000028188 V9084110 nonsynonymous NLRP11 CMX- CMX- chr19:56329447 G A Drastic NA G0000028188 V9084111 nonsynonymous NLRP11 CMX- CMX- chr19:56343378 C A Start codon NA G0000028188 V9084112 gained NLRP14 CMX- CMX- chr11:7091569 C T Drastic NA G0000016919 V9084115 nonsynonymous NLRP14 CMX- CMX- chr11:7079038 G A Drastic NA G0000016919 V9084116 nonsynonymous NLRP14 CMX- CMX- chr11:7059981 G A Drastic NA G0000016919 V9084117 nonsynonymous NLRP5 CMX- CMX- chr19:56569629 C G Drastic NA G0000028192 V9084120 nonsynonymous NLRP5 CMX- CMX- chr19:56572875 G A Drastic NA G0000028192 V9084121 nonsynonymous NLRP5 CMX- CMX- chr19:56567147 A G NA 8.96E−06 G0000028192 V9084170 NLRP5 CMX- CMX- chr19:56567133 A G NA 0.000422755 G0000028192 V9084169 NLRP8 CMX- CMX- chr19:56459342 C T Drastic NA G0000028191 V9084122 nonsynonymous NLRP8 CMX- CMX- chr19:56467375 C T Drastic NA G0000028191 V9084123 nonsynonymous NLRP8 CMX- CMX- chr19:56499279 G C Stop codon NA G0000028191 V9084124 lost PADI3 CMX- CMX- chr1:17548826- NA CNV PADI3 NA G0000000342 V1792728 18037716 gain (16 exons) PADI6 CMX- CMX- chr1:17707931 T G NA 0.000947202 G0000000344 V9084147 PADI6 CMX- CMX- chr1:17707757 C T NA 0.000791492 G0000000344 V9084145 PADI6 CMX- CMX- chr1:17707758 G C NA 0.000832422 G0000000344 V9084146 PAEP CMX- CMX- chr9:138131476- NA CNV PAEP NA G0000015254 V1271620 138644038 gain (2 exons) PLCB1 CMX- CMX- chr20:8142398- NA CNV PLCB1 NA G0000028445 V1930635 10362561 gain (2 exons) PMS2 CMX- CMX- chr7:6045627 C T Drastic NA G0000011251 V9084128 nonsynonymous PMS2 CMX- CMX- chr7:6029313 CNV NA Splice donor, NA G0000011251 SV00007 duplication acceptor and coding sequence PMS2 CMX- CMX- chr7:5981433 A G NA 0.000681822 G0000011251 V9084222 POF1B CMX- CMX- chrX:77243971- NA CNV POF1B NA G0000031099 V1507096 85734966 gain (15 exons) PRDM9 CMX- CMX- chr5:21969693- NA CNV PRDM9 NA G0000008219 V1222200 23940832 loss (3 exons) SCARB1 CMX- CMX- chr12:125270773 A G Drastic NA G0000019991 V9084131 nonsynonymous SCARB1 CMX- CMX- chr12:125323962 A C Start codon NA G0000019991 V9084132 gained SCARB1 CMX- CMX- chr12:125324570 C T Start codon NA G0000019991 V9084133 gained SCARB1 CMX- CMX- chr12:125324553 C T Start codon NA G0000019991 V9084134 gained SERPINA10 CMX- CMX- chr14:94691918- NA CNV SERPINA10 NA G0000021629 V1143735 278027 gain (4 exons) SIRT3 CMX- CMX- chr11:222921- NA CNV SIRT3 NA G0000016629 V1733950 278027 loss (2 exons) SPIN1 CMX- CMX- chr9:90754700 G A NA 0.000183378 G0000014689 V9084227 SPIN1 CMX- CMX- chr9:90754733 A C NA 0.000548473 G0000014689 V9084228 SPIN1 CMX- CMX- chr9:91120108 G A NA 0.000742923 G0000014689 V9084229 SPIN1 CMX- CMX- chr9:91120393 A G NA 0.000742923 G0000014689 V9084230 SPIN1 CMX- CMX- chr9:91124743 A G NA 0.000742923 G0000014689 V9084231 SPIN1 CMX- CMX- chr9:91126304 C T NA 0.000742923 G0000014689 V9084232 SPIN1 CMX- CMX- chr9:91126736 G A NA 0.00031089 G0000014689 V9084233 SPIN1 CMX- CMX- chr9:91130846 G A 0.000771149 G0000014689 V9084234 SPIN1 CMX- CMX- chr9:91131392 A G NA 0.000934759 G0000014689 V9084235 SPIN1 CMX- CMX- chr9:91133854 T A NA 0.000858194 G0000014689 V9084236 SPIN1 CMX- CMX- chr9:91139780 C T NA 0.000910019 G0000014689 V9084237 SPIN1 CMX- CMX- chr9:91146391 C T NA 0.000484881 G0000014689 V9084238 SPN CMX- CMX- chr16:29274955- NA CNV SPN NA G0000023664 V1697382 29761984 loss (1 exon) TACC3 CMX- CMX- chr4:1729556 G A Drastic NA G0000006818 V9084137 nonsynonymous TACC3 CMX- CMX- chr4:1732978 G A Drastic NA G0000006818 V9084138 nonsynonymous TLE6 CMX- CMX- chr19:2946999- NA CNV TLE6 G0000026639 V1806717 3051118 loss (2 exons) TLE6 CMX- CMX- chr19:2937389- NA CNV TLE6 NA G0000026639 V1336365 3057790 loss (2 exons) ZP3 CMX- CMX- chr7:7605876 G T Start codon NA G0000011947 V9084143 7 gained NA NA CMX- chr1:3584692- NA CNV NA 0.000363085 V2992389 3585200 gain NA NA CMX- chr1:33214881- NA CNV NA 0.00145087 V2992390 33216355 loss NA NA CMX- chr1:110252792- NA CNV NA 0.00145087 V2992391 110252792 loss NA NA CMX- chr1:148800056- NA CNV NA 0.000363942 V2992392 148802742 gain NA NA CMX- chr2:86414923- NA CNV NA 0.00145087 V2992393 86421116 loss NA NA CMX- chr2:96237124- NA CNV NA 1.33207E-05 V2992394 96237180 gain NA NA CMX- chr2:215404260- NA CNV NA 0.000269506 V2992395 215412550 loss NA NA CMX- chr2:217210720- NA CNV NA 0.00141334 V2992396 217210773 loss NA NA CMX- chr3:38475943- NA CNV NA 0.000263066 V2992397 38476013 loss NA NA CMX- chr3:150577148- NA CNV NA 0.00145087 V2992398 150583696 loss NA NA CMX- chr4:95892431- NA CNV NA 0.000595928 V2992399 95892748 loss NA NA CMX- chr4:103965296- NA CNV NA 9.32084E−05 V2992400 103966620 gain NA NA CMX- chr4:174691633- NA CNV NA 0.001024494 V2992401 174691747 loss NA NA CMX- chr5:106349950- NA CNV NA 0.001666446 V2992402 106350159 loss NA NA CMX- chr5:179654883- NA CNV NA 0.00091471 V2992403 179655477 loss NA NA CMX- chr6:77073676- NA CNV NA 0.00010917 V2992404 77085224 gain NA NA CMX- chr7:43968000- NA CNV NA 0.000860892 V2992405 44039304 loss NA NA CMX- chr7:69794356- NA CNV NA 0.00145087 V2992406 69800088 loss NA NA CMX- chr7:99464961- NA CNV NA 0.00125625 V2992407 99465782 loss NA NA CMX- chr7:101713977- NA CNV NA 0.000860892 V2992408 101923980 loss NA NA CMX- chr8:1229246- NA CNV NA 0.00116959 V2992409 101923980 gain NA NA CMX- chr8:141723436- NA CNV NA 0.001419478 V2992410 141723436 loss NA NA CMX- chr8:145465005- NA CNV NA 0.000488267 V2992411 145465005 loss NA NA CMX- chr9:119213636- NA NA NA 0.001446882 V2992412 119220054 NA NA CMX- chr9:129199955- NA CNV NA 0.00046153 V2992413 129200021 gain NA NA CMX- chr9:138557819- NA CNV NA 0.001446882 V2992414 138563454 loss NA NA CMX- chr10:13425201- NA CNV NA 0.000295719 V2992415 13426135 loss NA NA CMX- chr10:79352754- NA CNV NA 0.00145087 V2992416 79359886 loss NA NA CMX- chr10:135037958- NA CNV NA 0.000983276 V2992417 135044579 loss NA NA CMX- chr11:2113479- NA CNV NA 0.001566125 V2992418 2113533 loss NA NA CMX- chr11:20521659- NA CNV NA 0.001445217 V2992419 20533456 loss NA NA CMX- chr11:72165348- NA CNV NA 0.000366026 V2992420 72167302 loss NA NA CMX- chr12:110336347- NA CNV NA 0.000263066 V2992421 110344141 loss NA NA CMX- chr12:131580185- NA CNV NA 0.000434354 V2992422 131649282 loss NA NA CMX- chr13:105982985- NA CNV NA 0.000434354 V2992423 105988178 loss NA NA CMX- chr14:104711812- NA CNV NA 0.000117224 V2992424 104721574 loss NA NA CMX- chr14:105554845- NA CNV NA 0.00115304 V2992425 105554845 gain NA NA CMX- chr14:106038187- NA CNV NA 0.001388783 V2992426 106038187 gain NA NA CMX- chr15:72473905- NA CNV NA 2.2682E−05 V2992427 72483708 gain NA NA CMX- chr15:81743011- NA CNV NA 0.000934763 V2992428 81748883 loss NA NA CMX- chr15:97006211- NA CNV NA 0.00088514 V2992429 97006211 loss NA NA CMX- chr16:420035- NA CNV NA 0.001033484 V2992430 420035 loss NA NA CMX- chr16:28297962- NA CNV NA 3.83769E−05 V2992431 28340178 loss NA NA CMX- chr16:28614007- NA CNV NA 0.000337601 V2992432 28653740 loss NA NA CMX- chr16:33772936- NA CNV NA 0.001224595 V2992433 33809650 loss NA NA CMX- chr17:37686892- NA CNV NA 0.000263066 V2992434 37687211 loss NA NA CMX- chr17:70365673- NA CNV NA 0.001652185 V2992435 70365673 loss NA NA CMX- chr17:77418789- NA CNV NA 0.000117224 V2992436 77465794 loss NA NA CMX- chr19:1532671- NA CNV NA 0.000934076 V2992437 1549096 loss NA NA CMX- chr19:18835562- NA CNV NA 0.001224595 V2992438 18835562 loss NA NA CMX- chr19:38480199- NA CNV NA 0.000269506 V2992439 38480199 loss NA NA CMX- chr19:45731785- NA CNV NA 0.000229579 V2992440 45732555 loss NA NA CMX- chr19:53102000- NA CNV NA 0.001644428 V2992441 53153808 gain NA NA CMX- chr20:1500411- NA CNV NA 0.000461106 V2992442 1508282 loss NA NA CMX- chr20:6694925- NA CNV NA 0.000934763 V2992443 6696738 loss NA NA CMX- chr20:61592202- NA CNV NA 0.001494022 V2992444 61594834 loss NA NA CMX- chr21:15355967- NA CNV NA 0.001566125 V2992445 15355967 loss NA NA CMX- chr21:44541166- NA CNV NA 0.000257622 V2992446 44547084 loss NA NA CMX- chrX:100110102- NA CNV NA 0.001445217 V2992447 100110152 loss NA NA CMX- chrX:152934795- NA CNV NA 0.000247877 V2992448 152944222 loss Description of Certain Genes

Below are detailed descriptions of some of the fertility genes described in the tables above.

BARD1

BRCA1-Associated Ring Domain 1 (BARD1) is a gene that forms a heterodimer complex with the BRCA1 gene, and this complex is required for spindle-pole assembly in mitosis, and hence chromosome stability. Mouse embryos carrying homozygous null alleles for BARD1 died between embryonic day 7.5 and embryonic day 8.5 due to severely impaired cell proliferation (McCarthy et al. Molec. Cell. Biol. 23: 5056-5063, 2003).

C6orf221 (KHDC3L)

KH domain containing 3-like, subcortical maternal complex member (KHDC3L). The gene also has the identifier “C6orf221” [Entrez Gene id: 154288, HGNC id: 33699]. KH domains are protein domains that binds to RNA molecules, and KHDC3L is likely involved in genomic imprinting, a phenomenon where genes are expressed in a parental-origin specific manner. KHDC3L gene expression is maximal in germinal vesicle oocytes, tailing off through metaphase II oocytes, and its expression profile is similar to other oocyte-specific genes [Am J Hum Genet. 2011 Sep. 9; 89(3): 451-458]. It is also found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23]. Mice carrying homozygous null alleles for KHDC3L display a maternal effect defect in embryogenesis with delayed embryonic development and spindle abnormalities resulting in decreased litter sizes for homozygous females. In humans, KHDC3L has been implicated in familial biparental hydatidiform mole, a maternal-effect recessive inherited disorder [Ref: Am J Hum Genet. 2011 Sep. 9; 89(3): 451-458]

DNMT1

DNA (cytosine-5)-methyltransferase 1 (DNMT1) [Entrez Gene id: 1786, HGNC id: 2976], belongs to a group of enzymes that transfer methyl groups to position 5 of cytosine bases in DNA. While this process, known as DNA methylation, does not alter DNA base composition, it leaves “epigenetic” modifications to DNA molecules that affect the biochemical properties of the DNA region. DNA methylation, mediated by DNMT1, is crucial in determining cell fate during embyogenesis [Genes Dev. 2008 Jun. 15; 22(12):1607-16, Dev Biol. 2002 Jan. 1; 241(1):172-82.]. Mouse embryos carrying homozygous null alleles for DNMT1 survive only to mid-gestation. The expression of the DNMT1 gene is significantly higher in reproductive tissues than other cell types, and is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23, BMC Genomics. 2009 Aug. 3; 10:348].

FMR1

Fragile X Mental Retardation 1 (FMR1) encodes for the RNA-binding protein FMRP that is implicated in the fragile-X symdrome. The inhibition of translation may be a function of FMR1 in vivo, and that failure of mutant FMR1 protein to oligomerize may contribute to the pathophysiologic events leading to fragile X syndrome. Fragile X premutations in female carriers appear to be a risk factor for premature ovarian failure: 16% of the premutation carriers, menopause occurred before the age of 40, compared with none of the full-mutation carriers and 1 (0.4%) of the controls, indicating a significant association between premature menopause and premutation carrier status. [Am. J. Med. Genet. 83: 322-325, 1999]

FOXO3

Foxhead box O3 (FOXO3) encodes a protein that induces apoptosis in cells, lying within the DNA damage response and repair pathways. FOXO3 knockout female mice exhibit infertility phenotypes, in particular abnormal ovarian follicular function. Mice mutants carrying a homozygous non-synonymous substitution in exon 2 of the FOXO3 gene show loss of fertility of sexual maturity and exhibit premature ovarian failures. [Mammalian Genome 22: 235-248, 2011]

MUC4

MUC4 belongs to a family of high-molecular-weight glycoproteins that protect and lubricate the epithelial surface of respiratory, gastrointestinal and reproductive tracts. The extracellular domain can interact with an epidermal growth factor receptor on the cell surface to modulate downstream cell growth signaling by stabilizing and/or enhancing the activity of cell growth receptor complexes [Nature Rev. Cancer. 4(1):45-60, 2004]. MUC4 is expressed in the endometrial epithelium and is associated with endometriosis development and endometriosis-related infertility such as embryo implantation [BMC Med. 2011 9:19, 2011].

NLRP11

NLR family, pyrin domain containing 11 (NLRP11) encodes a leucine-rich protein belonging to a large family of proteins likely involved in inflammation [Nature Rev. Molec. Cell Biol. 4: 95-104, 2003], and is expressed in the ovary, testes and pre-implantation embryos [BMC Evol Biol. 2009 Aug. 14; 9:202. doi: 10.1186/1471-2148-9-202.]. NLRP11 gene expression shows specificity to reproductive tissues.

NLRP14

NLR family, pyrin domain containing 14 (NLRP14) encodes a leucine-rich protein belonging to a large family of proteins likely involved in inflammation [Nature Rev. Molec. Cell Biol. 4: 95-104, 2003], and is expressed in the ovary, testes and pre-implantation embryos [BMC Evol Biol. 2009 Aug. 14; 9:202. doi: 10.1186/1471-2148-9-202.]. NPRL14 is also found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23, BMC Genomics. 2009 Aug. 3; 10:348].

NLRP5

NLRP5 or MATER (Maternal antigen the embryos require), the protein encoded by the Nlrp5 gene, is another highly abundant oocyte protein that is essential in mouse for embryonic development beyond the two-cell stage. MATER was originally identified as an oocyte-specific antigen in a mouse model of autoimmune premature ovarian failure (Tong et al., 25 Endocrinology, 140:3720-3726, 1999). MATER demonstrates a similar expression and subcellular expression profile to PADI6. Like Padi6-null animals, Nlrp5-null females exhibit normal oogenesis, ovarian development, oocyte maturation, ovulation and fertilization. However, embryos derived from Nlrp5-null females undergo a developmental block at the two-cell stage and fail to exhibit normal embryonic genome activation (Tong et al., Nat Genet. 26:267-268, 2000; and Tong et al. Mamm Genome 11:281-287, 2000b).

NLRP8

NLR family, pyrin domain containing 8 (NLRP8) encodes a leucine-rich protein belonging to a large family of proteins likely involved in inflammation [Nature Rev. Molec. Cell Biol. 4: 95-104, 2003], and is expressed in the ovary, testes and pre-implantation embryos [BMC Evol Biol. 2009 Aug. 14; 9:202. doi: 10.1186/1471-2148-9-202.]. NLRP8 gene expression shows specificity to reproductive tissues.

NPM2

The gene NPM2[Entrez Gene id: 10361, HGNC id: 7930], or nucleoplasmin 2, is a chaperon that binds to histones, and is involved in sperm chromatin remodeling after oocyte entry [Nucleic Acids Res. 2012 June; 40(11): 4861-4878]. NPM2 has been found in a screen for oocyte-specific genes involved in preimplantation embryonic development [Semin Reprod Med. 2007 July; 25(4):243-51], and is differentially expressed during final oocyte maturation and early embryonic development in humans [Feral Steril. 2007 March; 87(3):677-90]. NPM2 is a maternal effect gene critical for nuclear and nucleolar organization and embryonic development, and is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23, BMC Genomics. 2009 Aug. 3; 10:348]. NPM2 is associated with abnormal oocyte morphology and reduced fertility in mice, and female mice homozygous null for NPM2 carry defects in preimplantation embryo development, with abnormalities in oocyte and early embryonic nuclei [Science. 2003 Apr. 25; 300(5619):633-6].

PADI6

Peptidylarginine Deiminase 6 (PADI6)

Padi6 was originally cloned from a 2D murine egg proteome gel based on its relative abundance, and Padi6 expression in mice appears to be almost entirely limited to the oocyte and pre-implantation embryo (Yurttas et al., 2010). Padi6 is first expressed in primordial oocyte follicles and persists, at the protein level, throughout pre-implantation development to the blastocyst stage (Wright et al., Dev Biol, 256:73-88, 2003). Inactivation of Padi6 leads to female infertility in mice, with the Padi6-null developmental arrest occurring at the two-cell stage (Yurttas et al., 2008).

PMS2

PMS2 is involved in DNA mismatch repair and involved in fertilization and pre-implantation development. It has been identified by knockout mouse studies as one of many maternal effect genes essential for development [Nature Cell Bio. 4 Suppl, pp.s 41-9].

SCARB1

Scavenger receptor class B, member 1 (SCARB1) gene encodes a glycoprotein that is a receptor for mediating cholesterol transport. SCARB1-null homozygous female mice were infertile with dysfunctional oocytes [J. Clin. Invest. 108: 1717-1722, 2001], hence, mutations in SCARB1 may affect female fertility by regulating lipoprotein metabolism.

SPIN1

Spindlin 1 (SPIN1) is a gene abundantly expressed in early embryo development, during the transition from oocyte to pluripotent early-embryo. SPIN1 is phosphorylated in a cell-cycle dependent manner and is associated with the meiotic spindle [Development 124: 493-503, 1997].

TACC3

Transforming, Acidic Coiled-Coil Containing Protein 3 (TACC3). In mice, TACC3 is abundantly expressed in the cytoplasm of growing oocytes, and is required for microtubule anchoring at the centrosome and for spindle assembly and cell survival (Fu et al., 2010). TACC3 is also found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23, BMC Genomics. 2009 Aug. 3; 10:348].

ZP1

Zona pellucid glycoprotein 1 (ZP1) encodes for a protein that is a structural component of the zona pellucida—an extracellular matrix that surrounds the oocyte and early embryo.

ZP2

Zona pellucid glycoprotein 2 (ZP2) encodes for a protein that is a structural component of the zona pellucida—an extracellular matrix that surrounds the oocyte and early embryo. ZP2 binds to acrosome-reacted sperm and is important in preventing polyspermy [Hum Reprod. 2004 July; 19(7):1580-6.].

ZP3

Zona pellucid glycoprotein 3 (ZP3) [Entrez Gene id: 7784, HGNC id: 13189], is a structural component of the zona pellucida—an extracellular matrix that surrounds the oocyte and early embryo. It is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [BMC Genomics. 2009 Aug. 3; 10:348]. ZP3 is also expressed in oocytes from early ovarian development, and likely to have a role in the development of primordial follicle before zona pellucida formation [Mol Cell Endocrinol. 2008 Jul. 16; 289(1-2):10-5]. Female mice carring null alleles for ZP3 exhibit decreased ovary size and weight, abnormal ovarian folliculogenesis and ovulation, ultimately resulting in female infertility.

ZP4

Zona pellucid glycoprotein 4 (ZP4) encodes for a protein that is a structural component of the zona pellucida—an extracellular matrix that surrounds the oocyte and early embryo. ZP4 stimulates acrosome reaction as part of a signaling pathway that involves Protein Kinase A [Biol Reprod. 2008 November; 79(5):869-77]

DNA (Cytosine-5)-Methyltransferase 1 (DNMT1)

[Entrez Gene id: 1786, HGNC id: 2976], belongs to a group of enzymes that transfer methyl groups to position 5 of cytosine bases in DNA. While this process, known as DNA methylation, does not alter DNA base composition, it leaves “epigenetic” modifications to DNA molecules that affect the biochemical properties of the DNA region. DNA methylation, mediated by DNMT1, is crucial in determining cell fate during embyogenesis [Genes Dev. 2008 Jun. 15; 22(12):1607-16, Dev Biol. 2002 Jan. 1; 241(1):172-82.]. Mouse embryos carrying homozygous null alleles for DNMT1 survive only to mid-gestation. The expression of the DNMT1 gene is significantly higher in reproductive tissues than other cell types, and is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23, BMC Genomics. 2009 Aug. 3; 10:348].

The gene NPM2 [Entrez Gene id: 10361, HGNC id: 7930], or nucleoplasmin 2, is a chaperon that binds to histones, and is involved in sperm chromatin remodeling after oocyte entry [Nucleic Acids Res. 2012 June; 40(11): 4861-4878]. NPM2 has been found in a screen for oocyte-specific genes involved in preimplantation embryonic development [Semin Reprod Med. 2007 July; 25(4):243-51], and is differentially expressed during final oocyte maturation and early embryonic development in humans [Feral Steril. 2007 March; 87(3):677-90]. NPM2 is a maternal effect gene critical for nuclear and nucleolar organization and embryonic development, and is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23, BMC Genomics. 2009 Aug. 3; 10:348]. NPM2 is associated with abnormal oocyte morphology and reduced fertility in mice, and female mice homozygous null for NPM2 carry defects in preimplantation embryo development, with abnormalities in oocyte and early embryonic nuclei [Science. 2003 Apr. 25; 300(5619):633-6].

Oocyte-Expressed Protein (OOEP)

[Entrez Gene id: 441161, HGNC id: 21382], also goes by the identifiers KHDC2, FLOPED, HOEP19 and C6orf156. OOEP is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [Reproduction. 2010 May; 139(5):809-23]. OOEP is expressed in ovaries, but not detectable in 11 other cell types including male testes. Within the ovary, its expression is restricted to growing oocytes. The OOEP protein product sublocalizes to the subcortex of eggs and preimplantation embryos. OOEP homozygous null female mice have seemingly normal ovarian physiology and produced viable eggs that can be fertilized, however, these embryos do not progress beyond cleavage stage development and hence these female mice are sterile. It is believed that a functioning OOEP is a pre-requisite for pre-implantation mouse development [Dev Cell. 2008 September; 15(3): 416-425.].

Factor Located in Oocytes Permitting Embryonic Development (FLOPED/OOEP)

The subcortical maternal complex (SCMC) is a poorly characterized murine oocyte structure to which several maternal effect gene products localize (Li et al. Dev Cell 15:416-425, 2008). PADI6, MATER, FILIA, TLE6, and FLOPED have been shown to localize to this complex (Li et al. Dev Cell 15:416-425, 2008; Yurttas et al. Development 135:2627-2636, 2008). This complex is not present in the absence of Floped and Nlrp5, and similar to embryos resulting from Nlrp5-depleted oocytes, embryos resulting from Floped-null oocytes do not progress past the two cell stage of mouse development (Li et al., 2008). FLOPED is a small (19 kD) RNA binding protein that has also been characterized under the name of MOEP19 (Herr et al., Dev Biol 314:300-316, 2008).

Zona Pellucid Glycoprotein 3 (ZP3)

[Entrez Gene id: 7784, HGNC id: 13189], is a structural component of the zona pellucida—an extracellular matrix that surrounds the oocyte and early embryo. It is found within the set of maternal factors that are important for driving egg-to-embryo transition during fertilization [BMC Genomics. 2009 Aug. 3; 10:348]. ZP3 is also expressed in oocytes from early ovarian development, and likely to have a role in the development of primordial follicle before zona pellucida formation [Mol Cell Endocrinol. 2008 Jul. 16; 289(1-2):10-5]. Female mice carring null alleles for ZP3 exhibit decreased ovary size and weight, abnormal ovarian folliculogenesis and ovulation, ultimately resulting in female infertility.

FIGLA (Factor in Germline Alpha)

[Entrez Gene id: 344018, HGNC id:], also goes by the gene identifiers POF6, BHLHC8, and FIGALPHA. This gene is a basic helix-loop-helix transcription factor that acts as an activator of oocyte genes. FIGLA is expressed in all ovarian follicular stages and in mature oocytes, and is required for normal folliculogenesis. FIGLA expression is also believed to repress genes expressed normal in male testes, and hence sustains the female phenotype by activating female and repressing male germ cell genetic hierarchies in growing oocytes during postnatal ovarian development [Mol Cell Biol. 2010 July; 30(14]. Female mice with FIGLA mutations result in decreased oocytes numbers and abnormal ovarian folliculogenesis. Heterozygous mutations in FIGLA has been implicated in women with premature ovarian failure [Am J Hum Genet. 2008 June; 82(6):1342-8.].

Peptidylarginine Deiminase 6 (PADI6)

Padi6 was originally cloned from a 2D murine egg proteome gel based on its relative abundance, and Padi6 expression in mice appears to be almost entirely limited to the oocyte and pre-implantation embryo (Yurttas et al., 2010). Padi6 is first expressed in primordial oocyte follicles and persists, at the protein level, throughout pre-implantation development to the blastocyst stage (Wright et al., Dev Biol, 256:73-88, 2003). Inactivation of Padi6 leads to female infertility in mice, with the Padi6-null developmental arrest occurring at the two-cell stage (Yurttas et al., 2008).

Maternal Antigen the Embryos Require (MATER/NLRP5)

MATER, the protein encoded by the Nlrp5 gene, is another highly abundant oocyte protein that is essential in for embryonic development beyond the two-cell stage. MATER was originally identified as an oocyte-specific antigen in a mouse model of autoimmune premature ovarian failure (Tong et al., Endocrinology, 140:3720-3726, 1999). MATER demonstrates a similar expression and subcellular expression profile to PADI6. Like Padi6-null animals, Nlrp5-null females exhibit normal oogenesis, ovarian development, oocyte maturation, ovulation and fertilization. However, embryos derived from Nlrp5-null females undergo a developmental block at the two-cell stage and fail to exhibit normal embryonic genome activation (Tong et al., Nat Genet. 26:267-268, 2000; and Tong et al. Mamm Genome 11:281-287, 2000b).

KH Domain Containing 3-Like, Subcortical Maternal Complex Member (FILIA/KHDC3L)

FILIA is another small RNA-binding domain containing maternally inherited murine protein. FILIA was identified and named for its interaction with MATER (Ohsugi et al. Development 135:259-269, 2008). Like other components of the SCMC, maternal inheritance of the Khdc3 gene product is required for early embryonic development. In mice, loss of Khdc3 results in a developmental arrest of varying severity with a high incidence of aneuploidy due, in part, to improper chromosome alignment during early cleavage divisions (Li et al., 2008). Khdc3 depletion also results in aneuploidy, due to spindle checkpoint assembly (SAC) inactivation, abnormal spindle assembly, and chromosome misalignment (Zheng et al. Proc Natl Acad Sci USA 106:7473-7478, 2009).

Basonuclin (BNC1)

Basonuclin is a zinc finger transcription factor that has been studied in mice. It is found expressed in keratinocytes and germ cells (male and female) and regulates rRNA (via polymerase I) and mRNA (via polymerase II) synthesis (Iuchi and Green, 1999; Wang et al., 2006). Depending on the amount by which expression is reduced in oocytes, embryos may not develop beyond the 8-cell stage. In Bsn1 depleted mice, a normal number of oocytes are ovulated even though oocyte development is perturbed, but many of these oocytes cannot go on to yield viable offspring (Ma et al., 2006).

Zygote Arrest 1 (ZAR1) Zar1 is an oocyte-specific maternal effect gene that is known to function at the oocyte to embryo transition in mice. High levels of Zar1 expression are observed in the cytoplasm of murine oocytes, and homozygous-null females are infertile: growing oocytes from Zar1-null females do not progress past the two-cell stage.

Cytosolic Phospholipase A2γ(PLA2G4C)

Under normal conditions, cPLA2γ, the protein product of the murine PLA2G4C ortholog, expression is restricted to oocytes and early embryos in mice. At the subcellular level, cPLA2γ mainly localizes to the cortical regions, nucleoplasm, and multivesicular aggregates of oocytes. It is also worth noting that while cPLA2γ expression does appear to be mainly limited to oocytes and pre-implantation embryos in healthy mice, expression is considerably up-regulated within the intestinal epithelium of mice infected with Trichinella spiralis. This suggests that cPLA2γ may also play a role in the inflammatory response. The human PLA2G4C differs in that rather than being abundantly expressed in the ovary, it is abundantly expressed in the heart and skeletal muscle. Also, the human protein contains a lipase consensus sequence but lacks a calcium-binding domain found in other PLA2 enzymes. Accordingly, another cytosolic phospholipase may be more relevant for human fertility.

Transforming, Acidic Coiled-Coil Containing Protein 3 (TACC3)

In mice, TACC3 is abundantly expressed in the cytoplasm of growing oocytes, and is required for microtubule anchoring at the centrosome and for spindle assembly and cell survival (Fu et al., 2010). In certain embodiments, the gene is a gene that is expressed in an oocyte. Exemplary genes include CTCF, ZFP57, POU5F1, SEBOX, and HDAC1.

In other embodiments, the gene is a gene that is involved in DNA repair pathways, including but not limited to, MLH1, PMS1 and PMS2. In other embodiments, the gene is BRCA1 or BRCA2.

In other embodiments, the biomarker is a gene product (e.g., RNA or protein) of an infertility-associated gene. In particular embodiments, the gene product is a gene product of a maternal effect gene. In other embodiments, the gene product is a product of a gene from Table 1. In certain embodiments, the gene product is a product of a gene that is expressed in an oocyte, such as a product of CTCF, ZFP57, POU5F1, SEBOX, and HDAC1. In other embodiments, the gene product is a product of a gene that is involved in DNA repair pathways, such as a product of MLH1, PMS1, or PMS2. In other embodiments, gene product is a product of BRCA1 or BRCA2.

In other embodiments, the biomarker may be an epigenetic factor, such as methylation patterns (e.g., hypermethylation of CpG islands), genomic localization or post-translational modification of histone proteins, or general post-translational modification of proteins such as acetylation, ubiquitination, phosphorylation, or others.

In other embodiments, methods of the invention analyze infertility-associated biomarkers in order to assess the risk infertility.

In certain embodiments, the biomarker is a genetic region, gene, or RNA/protein product of a gene associated with the one carbon metabolism pathway and other pathways that effect methylation of cellular macromolecules. Exemplary genes and products of those genes are described below.

Methylenetetrahydrofolate Reductase (MTHFR)

In particular embodiments a mutation (677C>T) in the MTHFR gene is associated with infertility. The enzyme 5,10-methylenetetrahydrofolate reductase regulates folate activity (Pavlik et al., Fertility and Sterility 95(7): 2257-2262, 2011). The 677TT genotype is known in the art to be associated with 60% reduced enzyme activity, inefficient folate metabolism, decreased blood folate, elevated plasma homocysteine levels, and reduced methylation capacity. Pavlik et al. (2011) investigated the effect of the MTHFR 677C>T on serum anti-Mullerian hormone (AMH) concentrations and on the numbers of oocytes retrieved (NOR) following controlled ovarian hyperstimulation (COH). Two hundred and seventy women undergoing COH for IVF were analyzed, and their AMH levels were determined from blood samples collected after 10 days of GnRH superagonist treatment and before COH. Average AMH levels of TT carriers were significantly higher than those of homozygous CC or heterozygous CT individuals. AMH serum concentrations correlated significantly with the NOR in all individuals studied. The study concluded that the MTHFR 677TT genotype is associated with higher serum AMH concentrations but paradoxically has a negative effect on NOR after COH. It was proposed that follicle maturation might be retarded in MTHFR 677TT individuals, which could subsequently lead to a higher proportion of initially recruited follicles that produce AMH, but fail to progress towards cyclic recruitment. The tissue gene expression patterns of MTHFR do not show any bias towards oocyte expression. Analyzing a sample for this mutation or other mutations (Table 1) in the MTHFR gene or abnormal gene expression of products of the MTHFR gene allows one to assess a risk of infertility.

Jeddi-Tehrani et al. (American Journal of Reproductive Immunology 66(2):149-156, 2011) investigated the effect of the MTHFR 677TT genotype on Recurrant Pregnancy Loss (RPL). One hundred women below 35 years of age with two successive pregnancy losses and one hundred healthy women with at least two normal pregnancies were used to assess the frequency of five candidate genetic risk factors for RPL-MTHFR 677C>T, MTHFR 1298A>C, PARI1-675 4G/5G (Plasminogen Activator Inhibitor-1 promoter region), BF-455G/A (Beta Fibrinogen promoter region), and ITGB3 1565T/C (Integrin Beta 3). The frequencies of the polymorphisms were calculated and compared between case and control groups. Both the MTHFR polymorphisms (677C>T and 1298 A>C) and the BF-455G/A polymorphism were found to be positively and ITGB3 1565T/C polymorphism was found to be negatively associated with RPL. Homozygosity but not heterozygosity for the PAI-1-6754G/5G polymorphism was significantly higher in patients with RPL than in the control group. The presence of both mutations of MTHFR genes highly increased the risk of RPL. Analyzing a sample for these mutation and other mutations (Table 1) in the MTHFR gene or abnormal gene expression of products of the MTHFR gene allows one to assess a risk of infertility.

Catechol-O-Methyltransferase (COMT)

In particular embodiments a mutation (472G>A) in the COMT gene is associated with infertility. Catechol-O-methyltransferase is known in the art to be one of several enzymes that inactivates catecholamine neurotransmitters by transferring a methyl group from SAM (S-adenosyl methionine) to the catecholamine. The AA gene variant is known to alter the enzyme's thermostability and reduces its activity 3 to 4 fold (Schmidt et al., Epidemiology 22(4): 476-485, 2011). Salih et al. (Fertility and Sterility 89(5, Supplement 1): 1414-1421, 2008) investigated the regulation of COMT expression in granulosa cells and assessed the effects of 2-ME2 (COMT product) and COMT inhibitors on DNA proliferation and steroidogenesis in JC410 porcine and HGLS human granulosa cell lines in in vitro experiments. They further assessed the regulation of COMT expression by DHT (Dihydrotestosterone), insulin, and ATRA (all-trans retinoic acid). They concluded that COMT expression in granulosa cells was up-regulated by insulin, DHT, and ATRA. Further, 2-ME2 decreased, and COMT inhibition increased granulosa cell proliferation and steroidogenesis. It was hypothesized that COMT overexpression with subsequent increased level of 2-ME2 may lead to ovulatory dysfunction. Analyzing a sample for this mutation in the COMT gene or abnormal gene expression of products of the COMT gene allows one to assess a risk of infertility.

Methionine Synthase Reductase (MTRR)

In particular embodiments a mutation (A66G) in the Methionine Synthase Reductase (MTRR) gene is associated with infertility. MTRR is required for the proper function of the enzyme Methionine Synthase (MTR). MTR converts homocysteine to methionine, and MTRR activates MTR, thereby regulating levels of homocysteine and methionine. The maternal variant A66G has been associated with early developmental disorders such as Down's syndrome (Pozzi et al., 2009) and Spina Bifida (Doolin et al., American journal of human genetics 71(5): 1222-1226, 2002). Analyzing a sample for this mutation in the MTRR gene or abnormal gene expression of products of the MTRR gene allows one to assess the risk of infertility.

Betaine-Homocysteine S-Methyltransferase (BHMT)

In particular embodiments a mutation (G716A) in the BHMT gene is associated with infertility. Betaine-Homocysteine S-Methyltransferase (BHMT), along with MTRR, assists in the Folate/B-12 dependent and choline/betaine-dependent conversions of homocysteine to methionine. High homocysteine levels have been linked to female infertility (Berker et al., Human Reproduction 24(9): 2293-2302, 2009). Benkhalifa et al. (2010) discuss that controlled ovarian hyperstimulation (COH) affects homocysteine concentration in follicular fluid. Using germinal vescicle oocytes from patients involved in IVF procedures, the study concludes that the human oocyte is able to regulate its homocysteine level via remethylation using MTR and BHMT, but not CBS (Cystathione Beta Synthase). They further emphasize that this may regulate the risk of imprinting problems during IVF procedures. Analyzing a sample for this mutation in the BHMT gene or abnormal gene expression of products of the BHMT gene allows one to assess a risk of infertility.

Ikeda et al. (Journal of Experimental Zoology Part A: Ecological Genetics and Physiology 313A(3): 129-136, 2010) examined the expression patterns of all methylation pathway enzymes in bovine oocytes and preimplantation embryos. Bovine oocytes were demonstrated to have the mRNA of MAT1A (Methionine adenosyltransferase), MAT2A, MAT2B, AHCY (S-adenosylhomocysteine hydrolase), MTR, BHMT, SHMT1 (Serine hydroxymethyltransferase), SHMT2, and MTHFR. All these transcripts were consistently expressed through all the developmental stages, except MAT1A, which was not detected from the 8-cell stage onward, and BHMT, which was not detected in the 8-cell stage. Furthermore, the effect of exogenous homocysteine on preimplantation development of bovine embryos was investigated in vitro. High concentrations of homocysteine induced hypermethylation of genomic DNA as well as developmental retardation in bovine embryos. Analyzing a sample for these irregular methylation patterns allows one to assess a risk of infertility.

Folate Receptor 2 (FOLR2)

In particular embodiments a mutation (rs2298444) in the FOLR2 gene is associated with infertility. Folate Receptor 2 helps transport folate (and folate derivatives) into cells. Elnakat and Ratnam (Frontiers in bioscience: a journal and virtual library 11: 506-519, 2006) implicate FOLR2, along with FOLR1, in ovarian and endometrial cancers. Analyzing sample mutations in the FOLR2 or FOLR1 genes or abnormal gene expression of products of the FOLR2 or FOLR1 genes allows one to assess a risk of infertility.

Transcobalamin 2 (TCN2)

In particular embodiments a mutation (C776G) in the TCN2 gene is associated with infertility. Transcobalamin 2 facilitates transport of cobalamin (Vitamin B12) into cells. Stanislawska-Sachadyn et al. (Eur J ClinNutr 64(11): 1338-1343, 2010) assessed the relationship between TCN2 776C>G polymorphism and both serum B12 and total homocysteine (tHcy) levels. Genotypes from 613 men from Northern Ireland were used to show that the TCN2 776CC genotype was associated with lower serum B12 concentrations when compared to the 776CG and 776GG genotypes. Furthermore, vitamin B12 status was shown to influence the relationship between TCN2 776C>G genotype and tHcy concentrations. The TCN2 776C>G polymorphism may contribute to the risk of pathologies associated with low B12 and high total homocysteine phenotype. Analyzing a sample for this mutation in the TCN2 gene or abnormal gene expression of products of the TCN2 gene allows one to assess a risk of infertility.

Cystathionine-Beta-Synthase (CBS)

In particular embodiments a mutation (rs234715) in the CBS gene is associated with infertility. With vitamin B6 as a cofactor, the Cystathionine-Beta-Synthase (CBS) enzyme catalyzes a reaction that permanently removes homocysteine from the methionine pathway by diverting it to the transsulfuration pathway. CBS gene mutations associated with decreased CBS activity also lead to elevated plasma homocysteine levels. Guzman et al. (2006) demonstrate that Cbs knockout mice are infertile. They further explain that Cbs-null female infertility is a consequence of uterine failure, which is a consequence of hyperhomocysteinemia or other factor(s) in the uterine environment. Analyzing a sample for this mutation in the CBS gene or abnormal gene expression of products of the CBS gene allows one to assess a risk of infertility.

In certain embodiments, the biomarker is a genetic region that has been previously associated with female infertility. A SNP association study by targeted re-sequencing was performed to search for new genetic variants associated with female infertility. Such methods have been successful in identifying significant variants associated in a wide range of diseases Rehman et al., 2010; Walsh et al., 2010). Briefly, a SNP association study is performed by collecting SNPs in genetic regions of interest in a number of samples and controls and then testing each of the SNPs that showed significant frequency differences between cases and controls. Significant frequency differences between cases and controls indicate that the SNP is associated with the condition of interest.

Assays

Methods of the invention involve conducting an assay that detects either a mutation in an infertility-associated gene or abnormal expression (over or under) of an infertility-associated gene product. In particular embodiments, the assay is conducted on infertility-associated genetic regions or products of these regions. Detailed descriptions of conventional methods, such as those employed to make and use nucleic acid arrays, amplification primers, hybridization probes, and the like can be found in standard laboratory manuals such as: Genome Analysis: A Laboratory

Manual Series (Vols. I-IV), Cold Spring Harbor Laboratory Press; PCR Primer: A Laboratory Manual, Cold Spring Harbor Laboratory Press; and Sambrook, J et al., (2001) Molecular Cloning: A Laboratory Manual, 2nd ed. (Vols. 1-3), Cold Spring Harbor Laboratory Press. Custom nucleic acid arrays are commercially available from, e.g., Affymetrix (Santa Clara, Calif.), Applied Biosystems (Foster City, Calif.), and Agilent Technologies (Santa Clara, Calif.).

Methods of detecting mutations in genetic regions are known in the art. In certain embodiments, a mutation in a single infertility-associated genetic region indicates infertility. In other embodiments, the assay is conducted on more than one genetic region, and a mutation in at least two of the genetic regions indicates infertility. In other embodiments, a mutation in at least three of the genetic regions indicates infertility; a mutation in at least four of the genetic regions indicates infertility; a mutation in at least five of the genetic regions indicates infertility; a mutation in at least six of the genetic regions indicates infertility; a mutation in at least seven of the genetic regions indicates infertility; a mutation in at least eight of the genetic regions indicates infertility; a mutation in at least nine of the genetic regions indicates infertility; a mutation in at least 10 of the genetic regions indicates infertility; a mutation in at least 15 of the genetic regions indicates infertility; or a mutation in all of the genetic regions from Table 1 indicates infertility.

In certain embodiments, a known single nucleotide polymorphism at a particular position can be detected by single base extension for a primer that binds to the sample DNA adjacent to that position. See for example Shuber et al. (U.S. Pat. No. 6,566,101), the content of which is incorporated by reference herein in its entirety. In other embodiments, a hybridization probe might be employed that overlaps the SNP of interest and selectively hybridizes to sample nucleic acids containing a particular nucleotide at that position. See for example Shuber et al. (U.S. Pat. Nos. 6,214,558 and 6,300,077), the content of which is incorporated by reference herein in its entirety.

In particular embodiments, nucleic acids are sequenced in order to detect variants (i.e., mutations) in the nucleic acid compared to wild-type and/or non-mutated forms of the sequence. The nucleic acid can include a plurality of nucleic acids derived from a plurality of genetic elements. Methods of detecting sequence variants are known in the art, and sequence variants can be detected by any sequencing method known in the art e.g., ensemble sequencing or single molecule sequencing.

Sequencing may be by any method known in the art. DNA sequencing techniques include classic dideoxy sequencing reactions (Sanger method) using labeled terminators or primers and gel separation in slab or capillary, sequencing by synthesis using reversibly terminated labeled nucleotides, pyrosequencing, 454 sequencing, allele specific hybridization to a library of labeled oligonucleotide probes, sequencing by synthesis using allele specific hybridization to a library of labeled clones that is followed by ligation, real time monitoring of the incorporation of labeled nucleotides during a polymerization step, polony sequencing, and SOLiD sequencing. Sequencing of separated molecules has more recently been demonstrated by sequential or single extension reactions using polymerases or ligases as well as by single or sequential differential hybridizations with libraries of probes.

One conventional method to perform sequencing is by chain termination and gel separation, as described by Sanger et al., Proc Natl. Acad. Sci. USA, 74(12): 5463 67 (1977). Another conventional sequencing method involves chemical degradation of nucleic acid fragments. See, Maxam et al., Proc. Natl. Acad. Sci., 74: 560 564 (1977). Methods have also been developed based upon sequencing by hybridization. See, e.g., Harris et al., (U.S. patent application number 2009/0156412). The content of each reference is incorporated by reference herein in its entirety.

A sequencing technique that can be used in the methods of the provided invention includes, for example, Helicos True Single Molecule Sequencing (tSMS) (Harris T. D. et al. (2008) Science 320:106-109). In the tSMS technique, a DNA sample is cleaved into strands of approximately 100 to 200 nucleotides, and a polyA sequence is added to the 3′ end of each DNA strand. Each strand is labeled by the addition of a fluorescently labeled adenosine nucleotide. The DNA strands are then hybridized to a flow cell, which contains millions of oligo-T capture sites that are immobilized to the flow cell surface. The templates can be at a density of about 100 million templates/cm². The flow cell is then loaded into an instrument, e.g., HeliScope™ sequencer, and a laser illuminates the surface of the flow cell, revealing the position of each template. A CCD camera can map the position of the templates on the flow cell surface. The template fluorescent label is then cleaved and washed away. The sequencing reaction begins by introducing a DNA polymerase and a fluorescently labeled nucleotide. The oligo-T nucleic acid serves as a primer. The polymerase incorporates the labeled nucleotides to the primer in a template directed manner. The polymerase and unincorporated nucleotides are removed. The templates that have directed incorporation of the fluorescently labeled nucleotide are detected by imaging the flow cell surface. After imaging, a cleavage step removes the fluorescent label, and the process is repeated with other fluorescently labeled nucleotides until the desired read length is achieved. Sequence information is collected with each nucleotide addition step. Further description of tSMS is shown for example in Lapidus et al. (U.S. Pat. No. 7,169,560), Lapidus et al. (U.S. patent application number 2009/0191565), Quake et al. (U.S. Pat. No. 6,818,395), Harris (U.S. Pat. No. 7,282,337), Quake et al. (U.S. patent application number 2002/0164629), and Braslaysky, et al., PNAS (USA), 100: 3960-3964 (2003), the contents of each of these references is incorporated by reference herein in its entirety.

Another example of a DNA sequencing technique that can be used in the methods of the provided invention is 454 sequencing (Roche) (Margulies, M et al. 2005, Nature, 437, 376-380). 454 sequencing involves two steps. In the first step, DNA is sheared into fragments of approximately 300-800 base pairs, and the fragments are blunt ended. Oligonucleotide adaptors are then ligated to the ends of the fragments. The adaptors serve as primers for amplification and sequencing of the fragments. The fragments can be attached to DNA capture beads, e.g., streptavidin-coated beads using, e.g., Adaptor B, which contains 5′-biotin tag. The fragments attached to the beads are PCR amplified within droplets of an oil-water emulsion. The result is multiple copies of clonally amplified DNA fragments on each bead. In the second step, the beads are captured in wells (pico-liter sized). Pyrosequencing is performed on each DNA fragment in parallel. Addition of one or more nucleotides generates a light signal that is recorded by a CCD camera in a sequencing instrument. The signal strength is proportional to the number of nucleotides incorporated. Pyrosequencing makes use of pyrophosphate (PPi) which is released upon nucleotide addition. PPi is converted to ATP by ATP sulfurylase in the presence of adenosine 5′ phosphosulfate. Luciferase uses ATP to convert luciferin to oxyluciferin, and this reaction generates light that is detected and analyzed.

Another example of a DNA sequencing technique that can be used in the methods of the provided invention is SOLiD technology (Applied Biosystems). In SOLiD sequencing, genomic DNA is sheared into fragments, and adaptors are attached to the 5′ and 3′ ends of the fragments to generate a fragment library. Alternatively, internal adaptors can be introduced by ligating adaptors to the 5′ and 3′ ends of the fragments, circularizing the fragments, digesting the circularized fragment to generate an internal adaptor, and attaching adaptors to the 5′ and 3′ ends of the resulting fragments to generate a mate-paired library. Next, clonal bead populations are prepared in microreactors containing beads, primers, template, and PCR components. Following PCR, the templates are denatured and beads are enriched to separate the beads with extended templates. Templates on the selected beads are subjected to a 3′ modification that permits bonding to a glass slide. The sequence can be determined by sequential hybridization and ligation of partially random oligonucleotides with a central determined base (or pair of bases) that is identified by a specific fluorophore. After a color is recorded, the ligated oligonucleotide is cleaved and removed and the process is then repeated.

Another example of a DNA sequencing technique that can be used in the methods of the provided invention is Ion Torrent sequencing (U.S. patent application numbers 2009/0026082, 2009/0127589, 2010/0035252, 2010/0137143, 2010/0188073, 2010/0197507, 2010/0282617, 2010/0300559), 2010/0300895, 2010/0301398, and 2010/0304982), the content of each of which is incorporated by reference herein in its entirety. In Ion Torrent sequencing, DNA is sheared into fragments of approximately 300-800 base pairs, and the fragments are blunt ended. Oligonucleotide adaptors are then ligated to the ends of the fragments. The adaptors serve as primers for amplification and sequencing of the fragments. The fragments can be attached to a surface and is attached at a resolution such that the fragments are individually resolvable. Addition of one or more nucleotides releases a proton (H⁺), which signal detected and recorded in a sequencing instrument. The signal strength is proportional to the number of nucleotides incorporated.

Another example of a sequencing technology that can be used in the methods of the provided invention is Illumina sequencing. Illumina sequencing is based on the amplification of DNA on a solid surface using fold-back PCR and anchored primers. Genomic DNA is fragmented, and adapters are added to the 5′ and 3′ ends of the fragments. DNA fragments that are attached to the surface of flow cell channels are extended and bridge amplified. The fragments become double stranded, and the double stranded molecules are denatured. Multiple cycles of the solid-phase amplification followed by denaturation can create several million clusters of approximately 1,000 copies of single-stranded DNA molecules of the same template in each channel of the flow cell. Primers, DNA polymerase and four fluorophore-labeled, reversibly terminating nucleotides are used to perform sequential sequencing. After nucleotide incorporation, a laser is used to excite the fluorophores, and an image is captured and the identity of the first base is recorded. The 3′ terminators and fluorophores from each incorporated base are removed and the incorporation, detection and identification steps are repeated.

Another example of a sequencing technology that can be used in the methods of the provided invention includes the single molecule, real-time (SMRT) technology of Pacific Biosciences. In SMRT, each of the four DNA bases is attached to one of four different fluorescent dyes. These dyes are phospholinked. A single DNA polymerase is immobilized with a single molecule of template single stranded DNA at the bottom of a zero-mode waveguide (ZMW). A ZMW is a confinement structure which enables observation of incorporation of a single nucleotide by DNA polymerase against the background of fluorescent nucleotides that rapidly diffuse in an out of the ZMW (in microseconds). It takes several milliseconds to incorporate a nucleotide into a growing strand. During this time, the fluorescent label is excited and produces a fluorescent signal, and the fluorescent tag is cleaved off. Detection of the corresponding fluorescence of the dye indicates which base was incorporated. The process is repeated.

Another example of a sequencing technique that can be used in the methods of the provided invention is nanopore sequencing (Soni G V and Meller A. (2007) Clin Chem 53: 1996-2001). A nanopore is a small hole, of the order of 1 nanometer in diameter. Immersion of a nanopore in a conducting fluid and application of a potential across it results in a slight electrical current due to conduction of ions through the nanopore. The amount of current which flows is sensitive to the size of the nanopore. As a DNA molecule passes through a nanopore, each nucleotide on the DNA molecule obstructs the nanopore to a different degree. Thus, the change in the current passing through the nanopore as the DNA molecule passes through the nanopore represents a reading of the DNA sequence.

Another example of a sequencing technique that can be used in the methods of the provided invention involves using a chemical-sensitive field effect transistor (chemFET) array to sequence DNA (for example, as described in US Patent Application Publication No. 20090026082). In one example of the technique, DNA molecules can be placed into reaction chambers, and the template molecules can be hybridized to a sequencing primer bound to a polymerase. Incorporation of one or more triphosphates into a new nucleic acid strand at the 3′ end of the sequencing primer can be detected by a change in current by a chemFET. An array can have multiple chemFET sensors. In another example, single nucleic acids can be attached to beads, and the nucleic acids can be amplified on the bead, and the individual beads can be transferred to individual reaction chambers on a chemFET array, with each chamber having a chemFET sensor, and the nucleic acids can be sequenced.

Another example of a sequencing technique that can be used in the methods of the provided invention involves using a electron microscope (Moudrianakis E. N. and Beer M. Proc Natl Acad Sci USA. 1965 March; 53:564-71). In one example of the technique, individual DNA molecules are labeled using metallic labels that are distinguishable using an electron microscope. These molecules are then stretched on a flat surface and imaged using an electron microscope to measure sequences.

If the nucleic acid from the sample is degraded or only a minimal amount of nucleic acid can be obtained from the sample, PCR can be performed on the nucleic acid in order to obtain a sufficient amount of nucleic acid for sequencing (See e.g., Mullis et al. U.S. Pat. No. 4,683,195, the contents of which are incorporated by reference herein in its entirety).

Methods of detecting levels of gene products (e.g., RNA or protein) are known in the art. Commonly used methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247 283 (1999), the contents of which are incorporated by reference herein in their entirety); RNAse protection assays (Hod, Biotechniques 13:852 854 (1992), the contents of which are incorporated by reference herein in their entirety); and PCR-based methods, such as reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263 264 (1992), the contents of which are incorporated by reference herein in their entirety). Alternatively, antibodies may be employed that can recognize specific duplexes, including RNA duplexes, DNA-RNA hybrid duplexes, or DNA-protein duplexes. Other methods known in the art for measuring gene expression (e.g., RNA or protein amounts) are shown in Yeatman et al. (U.S. patent application number 2006/0195269), the content of which is hereby incorporated by reference in its entirety.

A differentially expressed gene or differential gene expression refer to a gene whose expression is activated to a higher or lower level in a subject suffering from a disorder, such as infertility, relative to its expression in a normal or control subject. The terms also include genes whose expression is activated to a higher or lower level at different stages of the same disorder. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product. Such differences may be evidenced by a change in mRNA levels, surface expression, secretion or other partitioning of a polypeptide, for example.

Differential gene expression may include a comparison of expression between two or more genes or their gene products, or a comparison of the ratios of the expression between two or more genes or their gene products, or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disorder, such as infertility, or between various stages of the same disorder. Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products. Differential gene expression (increases and decreases in expression) is based upon percent or fold changes over expression in normal cells. Increases may be of 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, or 200% relative to expression levels in normal cells. Alternatively, fold increases may be of 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10 fold over expression levels in normal cells. Decreases may be of 1, 5, 10, 20, 30, 40, 50, 55, 60, 65, 70, 75, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 99 or 100% relative to expression levels in normal cells.

In certain embodiments, reverse transcriptase PCR (RT-PCR) is used to measure gene expression. RT-PCR is a quantitative method that can be used to compare mRNA levels in different sample populations to characterize patterns of gene expression, to discriminate between closely related mRNAs, and to analyze RNA structure.

The first step is the isolation of mRNA from a target sample. The starting material is typically total RNA isolated from human tissues or fluids.

General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67 (1987), and De Andres et al., BioTechniques 18:42044 (1995). The contents of each of theses references is incorporated by reference herein in their entirety. In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MASTERPURE Complete DNA and RNA Purification Kit (EPICENTRE, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

The first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are avilo myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the circumstances and the goal of expression profiling. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer's instructions. The derived cDNA can then be used as a template in the subsequent PCR reaction.

Although the PCR step can use a variety of thermostable DNA-dependent DNA polymerases, it typically employs the Taq DNA polymerase, which has a 5′-3′ nuclease activity but lacks a 3′-5′ proofreading endonuclease activity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activity of Taq polymerase to hydrolyze a hybridization probe bound to its target amplicon, but any enzyme with equivalent 5′ nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. A third oligonucleotide, or probe, is designed to detect nucleotide sequence located between the two PCR primers. The probe is non-extendible by Taq DNA polymerase enzyme, and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are located close together as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resultant probe fragments disassociate in solution, and signal from the released reporter dye is free from the quenching effect of the second fluorophore. One molecule of reporter dye is liberated for each new molecule synthesized, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, ABI PRISM 7700 Sequence Detection System™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In certain embodiments, the 5′ nuclease procedure is run on a real-time quantitative PCR device such as the ABI PRISM 7700™ Sequence Detection System™. The system consists of a thermocycler, laser, charge-coupled device (CCD), camera and computer. The system amplifies samples in a 96-well format on a thermocycler. During amplification, laser-induced fluorescent signal is collected in real-time through fiber optics cables for all 96 wells, and detected at the CCD. The system includes software for running the instrument and for analyzing the data.

5′-Nuclease assay data are initially expressed as Ct, or the threshold cycle. As discussed above, fluorescence values are recorded during every cycle and represent the amount of product amplified to that point in the amplification reaction. The point when the fluorescent signal is first recorded as statistically significant is the threshold cycle (C_(t)).

To minimize errors and the effect of sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard is expressed at a constant level among different tissues, and is unaffected by the experimental treatment. RNAs most frequently used to normalize patterns of gene expression are mRNAs for the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and β-actin. For performing analysis on pre-implantation embryos and oocytes, Chuk is a gene that is used for normalization.

A more recent variation of the RT-PCR technique is the real time quantitative PCR, which measures PCR product accumulation through a dual-labeled fluorigenic probe (i.e., TaqMan® probe). Real time PCR is compatible both with quantitative competitive PCR, in which internal competitor for each target sequence is used for normalization, and with quantitative comparative PCR using a normalization gene contained within the sample, or a housekeeping gene for RT-PCR. For further details see, e.g. Held et al., Genome Research 6:986 994 (1996), the contents of which are incorporated by reference herein in their entirety.

In another embodiment, a MassARRAY-based gene expression profiling method is used to measure gene expression. In the MassARRAY-based gene expression profiling method, developed by Sequenom, Inc. (San Diego, Calif.) following the isolation of RNA and reverse transcription, the obtained cDNA is spiked with a synthetic DNA molecule (competitor), which matches the targeted cDNA region in all positions, except a single base, and serves as an internal standard. The cDNA/competitor mixture is PCR amplified and is subjected to a post-PCR shrimp alkaline phosphatase (SAP) enzyme treatment, which results in the dephosphorylation of the remaining nucleotides. After inactivation of the alkaline phosphatase, the PCR products from the competitor and cDNA are subjected to primer extension, which generates distinct mass signals for the competitor- and cDNA-derives PCR products. After purification, these products are dispensed on a chip array, which is pre-loaded with components needed for analysis with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. The cDNA present in the reaction is then quantified by analyzing the ratios of the peak areas in the mass spectrum generated. For further details see, e.g. Ding and Cantor, Proc. Natl. Acad. Sci. USA 100:3059 3064 (2003).

Further PCR-based techniques include, for example, differential display (Liang and Pardee, Science 257:967 971 (1992)); amplified fragment length polymorphism (iAFLP) (Kawamoto et al., Genome Res. 12:1305 1312 (1999)); BeadArray™ technology (Illumina, San Diego, Calif.; Oliphant et al., Discovery of Markers for Disease (Supplement to Biotechniques), June 2002; Ferguson et al., Analytical Chemistry 72:5618 (2000)); BeadsArray for Detection of Gene Expression (BADGE), using the commercially available Luminex100 LabMAP system and multiple color-coded microspheres (Luminex Corp., Austin, Tex.) in a rapid assay for gene expression (Yang et al., Genome Res. 11:1888 1898 (2001)); and high coverage expression profiling (HiCEP) analysis (Fukumura et al., Nucl. Acids. Res. 31(16) e94 (2003)). The contents of each of which are incorporated by reference herein in their entirety.

In certain embodiments, differential gene expression can also be identified, or confirmed using a microarray technique. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Methods for making microarrays and determining gene product expression (e.g., RNA or protein) are shown in Yeatman et al. (U.S. patent application number 2006/0195269), the content of which is incorporated by reference herein in its entirety.

In a specific embodiment of the microarray technique, PCR amplified inserts of cDNA clones are applied to a substrate in a dense array, for example, at least 10,000 nucleotide sequences are applied to the substrate. The microarrayed genes, immobilized on the microchip at 10,000 elements each, are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair-wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. The miniaturized scale of the hybridization affords a convenient and rapid evaluation of the expression pattern for large numbers of genes. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106 149 (1996), the contents of which are incorporated by reference herein in their entirety). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip technology, or Incyte's microarray technology.

Alternatively, protein levels can be determined by constructing an antibody microarray in which binding sites comprise immobilized, preferably monoclonal, antibodies specific to a plurality of protein species encoded by the cell genome. Preferably, antibodies are present for a substantial fraction of the proteins of interest. Methods for making monoclonal antibodies are well known (see, e.g., Harlow and Lane, 1988, ANTIBODIES: A LABORATORY MANUAL, Cold Spring Harbor, N.Y., which is incorporated in its entirety for all purposes). In one embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell. With such an antibody array, proteins from the cell are contacted to the array, and their binding is assayed with assays known in the art. Generally, the expression, and the level of expression, of proteins of diagnostic or prognostic interest can be detected through immunohistochemical staining of tissue slices or sections.

Finally, levels of transcripts of marker genes in a number of tissue specimens may be characterized using a “tissue array” (Kononen et al., Nat. Med. 4(7):844-7 (1998)). In a tissue array, multiple tissue samples are assessed on the same microarray. The arrays allow in situ detection of RNA and protein levels; consecutive sections allow the analysis of multiple samples simultaneously.

In other embodiments, Serial Analysis of Gene Expression (SAGE) is used to measure gene expression. Serial analysis of gene expression (SAGE) is a method that allows the simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript. First, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules, that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag. For more details see, e.g. Velculescu et al., Science 270:484 487 (1995); and Velculescu et al., Cell 88:243 51 (1997, the contents of each of which are incorporated by reference herein in their entirety).

In other embodiments Massively Parallel Signature Sequencing (MPSS) is used to measure gene expression. This method, described by Brenner et al., Nature Biotechnology 18:630 634 (2000), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. This is followed by the assembly of a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3×10⁶ microbeads/cm²). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast cDNA library.

Immunohistochemistry methods are also suitable for detecting the expression levels of the gene products of the present invention. Thus, antibodies (monoclonal or polyclonal) or antisera, such as polyclonal antisera, specific for each marker are used to detect expression. The antibodies can be detected by direct labeling of the antibodies themselves, for example, with radioactive labels, fluorescent labels, hapten labels such as, biotin, or an enzyme such as horse radish peroxidase or alkaline phosphatase. Alternatively, unlabeled primary antibody is used in conjunction with a labeled secondary antibody, comprising antisera, polyclonal antisera or a monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.

In certain embodiments, a proteomics approach is used to measure gene expression. A proteome refers to the totality of the proteins present in a sample (e.g. tissue, organism, or cell culture) at a certain point of time. Proteomics includes, among other things, study of the global changes of protein expression in a sample (also referred to as expression proteomics). Proteomics typically includes the following steps: (1) separation of individual proteins in a sample by 2-D gel electrophoresis (2-D PAGE); (2) identification of the individual proteins recovered from the gel, e.g. my mass spectrometry or N-terminal sequencing, and (3) analysis of the data using bioinformatics. Proteomics methods are valuable supplements to other methods of gene expression profiling, and can be used, alone or in combination with other methods, to detect the products of the prognostic markers of the present invention.

In some embodiments, mass spectrometry (MS) analysis can be used alone or in combination with other methods (e.g., immunoassays or RNA measuring assays) to determine the presence and/or quantity of the one or more biomarkers disclosed herein in a biological sample. In some embodiments, the MS analysis includes matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) MS analysis, such as for example direct-spot MALDI-TOF or liquid chromatography MALDI-TOF mass spectrometry analysis. In some embodiments, the MS analysis comprises electrospray ionization (ESI) MS, such as for example liquid chromatography (LC) ESI-MS. Mass analysis can be accomplished using commercially-available spectrometers. Methods for utilizing MS analysis, including MALDI-TOF MS and ESI-MS, to detect the presence and quantity of biomarker peptides in biological samples are known in the art. See for example U.S. Pat. Nos. 6,925,389; 6,989,100; and 6,890,763 for further guidance, each of which is incorporated by reference herein in their entirety.

Phenotypic Traits

In certain embodiments, methods of the invention assess risk of female infertility by correlating assay results with an analysis of a phenotypic trait or environmental exposure that may be associated with infertility. Exemplary phenotypic traits or environmental exposures are shown in Table 8.

TABLE 8 Phenotypic and environmental variables impacting fertility success Cholesterol levels on different days of the menstrual cycle Age of first menses for patient and female blood relatives (e.g. sisters, mother, grandmothers) Age of menopause for female blood relatives (e.g. sisters, mother, grandmothers) Number of previous pregnancies (biochemical/ectopic/clinical/ fetal heart beat detected, live birth outcomes), age at the time, and outcome for patient and female blood relatives (e.g. sisters, mother, grandmothers) Diagnosis of Polycystic Ovarian Syndrome History of hydrosalpinx or tubal occlusion History of endometriosis, pelvic pain, or painful periods Cancer history/type of cancer/treatment/outcome for patient and female blood relatives (e.g. sisters, mother, grandmothers) Age that sexual activity began, current level of sexual activity Smoking history for patient and blood relatives Travel schedule/number of flying hours a year/time difference changes of more than 3 hours (Jetlag and Flight-associated Radiation Exposure) Nature of periods (length of menses, length of cycle) Biological age (number of years since first menses) Birth control use Drug use (illegal or legal) Body mass index (current, lowest ever, highest ever) History of polyps History of hormonal imbalance History of amenorrhoea History of eating disorders Alcohol consumption by patient or blood relatives Details of mother's pregnancy with patient (i.e. measures of uterine environment): any drugs taken, smoking, alcohol, stress levels, exposure to plastics (i.e. Tupperware), composition of diet (see below) Sleep patterns: number of hours a night, continuous/overall Diet: meat, organic produce, vegetables, vitamin or other supplement consumption, dairy (full fat or reduced fat), coffee/tea consumption, folic acid, sugar(complex, artificial, simple), processed food versus home cooked. Exposure to plastics: microwave in plastic, cook with plastic, store food in plastic, plastic water or coffee mugs. Water consumption: amount per day, format: straight from the tap, bottled water (plastic or bottle), filtered (type: e.g. Britta/Pur) Residence history starting with mother's pregnancy: location/duration Environmental exposure to potential toxins for different regions (extracted from government monitoring databases) Health metrics: autoimmune disease, chronic illness/condition Pelvic surgery history Life time number of pelvic X-rays History of sexually transmitted infections: type/treatment/outcome Reproductive hormone levels: follicle stimulating hormone, anti-Miillerian hormone, estrogen, progesterone Stress Thickness and type of endometrium throughout the menstrual cycle. Age Height Fertility treatment history and details: history of hormone stimulation, brand of drugs used, basal antral follicle count, follicle count after stimulation with different protocols, number/quality/stage of retrieved oocytes/ development profile of embryos resulting from in vitro insemination (natural or ICSI), details of IVF procedure (which clinic, doctor/embryologist at clinic, assisted hatching, fresh or thawed oocytes/embryos, embryo transfer (blood on the catheter/squirt detection and direction on ultrasound), number of successful and unsuccessful IVF attempts Morning sickness during pregnancy Breast size before/during/after pregnancy History of ovarian cysts Twin or sibling from multiple birth (mono-zygotic or di-zygotic) Male factor infertility for reproductive partner: Semen analysis (count, motility, morphology), Vasectomy, male cancer, smoking, alcohol, diet, STIs Blood type DES exposure in utero Past and current exercise/athletic history Levels of phthalates, including metabolites: MEP - monoethyl phthalate, MECPP - mono(2-ethyl-5-carboxypentyl) phthalate, MEHHP - mono(2-ethy1-5-hydroxyhexyl) phthalate, MEOHP - mono(2-ethyl-5-ox-ohexyl) phthalate, MBP - monobutyl phthalate, MBzP - monobenzyl phthalate, MEHP - mono(2-ethylhexyl) phthalate, MiBP - mono-isobutyl phthalate, MCPP - mono(3-carboxypropyl) phthalate, MCOP - monocarboxyisooctyl phthalate, MCNP - monocarboxyisononyl phthalate Familial history of Premature Ovarian Failure/Insufficiency Autoimmunity history - Antiadrenal antibodies (anti-21-hydroxylase antibodies), antiovarian antibodies, antithyroid anitibodies (anti-thyroid peroxidase, antithyroglobulin) Hormone levels: Leutenizing hormone (using immunofluorometric assay), Δ4-Androstenedione (using radioimmunoassay), Dehydroepiandrosterone (using radioimmunoassay), and Inhibin B (commercial ELISA) Number of years trying to conceive Dioxin and PVC exposure Hair color Nevi (moles) Lead, cadmium, and other heavy metal exposure

Information regarding the fertility-associated phenotypic traits of the female, such as those listed in Table 8, can be obtained by any means known in the art. In many cases, such information can be obtained from a questionnaire completed by the subject that contains questions regarding certain fertility-associated phenotypic traits. Additional information can be obtained from a questionnaire completed by the subject's partner and blood relatives. The questionnaire includes questions regarding the subject's fertility-associated phenotypic traits, such as her age, smoking habits, or frequency of alcohol consumption. Information can also be obtained from the medical history of the subject, as well as the medical history of blood relatives and other family members. Additional information can be obtained from the medical history and family medical history of the subject's partner. Medical history information can be obtained through analysis of electronic medical records, paper medical records, a series of questions about medical history included in the questionnaire, and a combination thereof. In other cases, the information can be obtained by analyzing a sample collected from the female subject, reproductive partners of the subject, blood relatives of the subject, and a combination thereof. The sample may include human tissue or bodily fluid. Any of the assays described herein may be used to obtain the phenotypic trait.

In other embodiments, an assay specific to an environmental exposure is used to obtain the phenotypic trait of interest. Such assays are known to those of skill in the art, and may be used with methods of the invention. For example, the hormones used in birth control pills (estrogen and progesterone) may be detected from a urine or blood test. Venners et al. (Hum. Reprod. 21(9): 2272-2280, 2006) reports assays for detecting estrogen and progesterone in urine and blood samples. Venner also reports assays for detecting the chemicals used in fertility treatments.

Similarly, illicit drug use may be detected from a tissue or body fluid, such as hair, urine sweat, or blood, and there are numerous commercially available assays (LabCorp) for conducting such tests. Standard drug tests look for ten different classes of drugs, and the test is commercially known as a “10-panel urine screen”. The 10-panel urine screen consists of the following: 1. Amphetamines (including Methamphetamine) 2. Barbiturates 3. Benzodiazepines 4. Cannabinoids (THC)₅. Cocaine 6. Methadone 7. Methaqualone 8. Opiates (Codeine, Morphine, Heroin, Oxycodone, Vicodin, etc.) 9. Phencyclidine (PCP) 10. Propoxyphene. Use of alcohol can also be detected by such tests.

Numerous assays can be used to tests a patient's exposure to plastics (e.g., Bisphenol A (BPA)). BPA is most commonly found as a component of polycarbonates (about 74% of total BPA produced) and in the production of epoxy resins (about 20%). As well as being found in a myriad of products including plastic food and beverage contains (including baby and water bottles), BPA is also commonly found in various household appliances, electronics, sports safety equipment, adhesives, cash register receipts, medical devices, eyeglass lenses, water supply pipes, and many other products. Assays for testing blood, sweat, or urine for presence of BPA are described, for example, in Genuis et al. (Journal of Environmental and Public Health, Volume 2012, Article ID 185731, 10 pages, 2012).

Association studies can be performed to analyze the effect of genetic mutations or abnormal gene expression on a particular trait being studied. Infertility as a trait may be analyzed as a non-continuous variable in a case-control study that includes as the patients infertile females and as controls fertile females that are age and ethnically matched. Methods including logistic regression analysis and Chi square tests may be used to identify an association between genetic mutations or abnormal gene expression and infertility. In addition, when using logistic regression, adjustments for covariates like age, smoking, BMI and other factors that effect infertility, such as those shown in Table 4, may be included in the analysis.

In addition, haplotype effects can be estimated using programs such as Haploscore. Alternatively, programs such as Haploview and Phase can be used to estimate haplotype frequencies and then further analysis such as Chi square test can be performed. Logistic regression analysis may be used to generate an odds ratio and relative risk for each genetic variant or variants.

The association between genetic mutations and/or abnormal gene expression and infertility may be analyzed within cases only or comparing cases and controls using analysis of variance. Such analysis may include, adjustments for covariates like age, smoking, BMI and other factors that effect infertility. In addition, haplotype effects can be estimated using programs such as Haploscore.

Method of logistic regression are described, for example in, Ruczinski (Journal of Computational and Graphical Statistics 12:475-512, 2003); Agresti (An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc., 1996, New York, Chapter 8); and Yeatman et al. (U.S. patent application number 2006/0195269), the content of each of which is hereby incorporated by reference in its entirety.

Other algorithms for analyzing associations are known. For example, the stochastic gradient boosting is used to generate multiple additive regression tree (MART) models to predict a range of outcome probabilities. Each tree is a recursive graph of decisions the possible consequences of which partition patient parameters; each node represents a question (e.g., is the FSH level greater than x?) and the branch taken from that node represents the decision made (e.g. yes or no). The choice of question corresponding to each node is automated. A MART model is the weighted sum of iteratively produced regression trees. At each iteration, a regression tree is fitted according to a criterion in which the samples more involved in the prediction error are given priority. This tree is added to the existing trees, the prediction error is recalculated, and the cycle continues, leading to a progressive refinement of the prediction. The strengths of this method include analysis of many variables without knowledge of their complex interactions beforehand.

A different approach called the generalized linear model, expresses the outcome as a weighted sum of functions of the predictor variables. The weights are calculated based on least squares or Bayesian methods to minimize the prediction error on the training set. A predictor's weight reveals the effect of changing that predictor, while holding the others constant, on the outcome. In cases where one or more predictors are highly correlated, in a phenomenon known as collinearity, the relative values of their weights are less meaningful; steps must be taken to remove that collinearity, such as by excluding the nearly redundant variables from the model. Thus, when properly interpreted, the weights express the relative importance of the predictors. Less general formulations of the generalized linear model include linear regression, multiple regression, and multifactor logistic regression models, and are highly used in the medical community as clinical predictors.

Microarrays

In certain aspects, the invention provides a microarray including a plurality of oligonucleotides attached to a substrate at discrete addressable positions, in which at least one of the oligonucleotides hybridizes to a portion of a genetic region from Table 1 that includes an infertility-associated mutation.

Methods of constructing microarrays are known in the art. See for example Yeatman et al. (U.S. patent application number 2006/0195269), the content of which is hereby incorporated by reference in its entirety.

Microarrays are prepared by selecting probes that include a polynucleotide sequence, and then immobilizing such probes to a solid support or surface. For example, the probes may comprise DNA sequences, RNA sequences, or copolymer sequences of DNA and RNA. The polynucleotide sequences of the probes may also comprise DNA and/or RNA analogues, or combinations thereof. For example, the polynucleotide sequences of the probes may be full or partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthesized nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences can be synthesized either enzymatically in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

The probe or probes used in the methods of the invention are preferably immobilized to a solid support, which may be either porous or non-porous. For example, the probes of the invention may be polynucleotide sequences, which are attached to a nitrocellulose or nylon membrane or filter covalently at either the 3′ or the 5′ end of the polynucleotide. Such hybridization probes are well known in the art (see, e.g., Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989). Alternatively, the solid support or surface may be a glass or plastic surface. In a particularly preferred embodiment, hybridization levels are measured to microarrays of probes consisting of a solid phase on the surface of which are immobilized a population of polynucleotides, such as a population of DNA or DNA mimics, or, alternatively, a population of RNA or RNA mimics. The solid phase may be a nonporous or, optionally, a porous material such as a gel.

In preferred embodiments, a microarray comprises a support or surface with an ordered array of binding (e.g., hybridization) sites or “probes” each representing one of the genes described herein, particularly the genes described in Table 1. Preferably the microarrays are addressable arrays, and more preferably positionally addressable arrays. More specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). In preferred embodiments, each probe is covalently attached to the solid support at a single site.

Microarrays can be made in a number of ways, of which several are described below. However produced, microarrays share certain characteristics. The arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared with each other. Preferably, microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. The microarrays are preferably small, e.g., between 1 cm² and 25 cm², between 12 cm² and 13 cm², or 3 cm². However, larger arrays are also contemplated and may be preferable, e.g., for use in screening arrays. Preferably, a given binding site or unique set of binding sites in the microarray will specifically bind (e.g., hybridize) to the product of a single gene in a cell (e.g., to a specific mRNA, or to a specific cDNA derived therefrom). However, in general, other related or similar sequences will cross hybridize to a given binding site.

The microarrays of the present invention include one or more test probes, each of which has a polynucleotide sequence that is complementary to a subsequence of RNA or DNA to be detected. Preferably, the position of each probe on the solid surface is known. Indeed, the microarrays are preferably positionally addressable arrays. Specifically, each probe of the array is preferably located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position on the array (i.e., on the support or surface).

According to the invention, the microarray is an array (i.e., a matrix) in which each position represents one of the biomarkers described herein. For example, each position can contain a DNA or DNA analogue based on genomic DNA to which a particular RNA or cDNA transcribed from that genetic marker can specifically hybridize. The DNA or DNA analogue can be, e.g., a synthetic oligomer or a gene fragment. In one embodiment, probes representing each of the markers is present on the array. In a preferred embodiment, the array comprises probes for each of the genes listed in Table 1.

As noted above, the probe to which a particular polynucleotide molecule specifically hybridizes according to the invention contains a complementary genomic polynucleotide sequence. The probes of the microarray preferably consist of nucleotide sequences of no more than 1,000 nucleotides. In some embodiments, the probes of the array consist of nucleotide sequences of 10 to 1,000 nucleotides. In a preferred embodiment, the nucleotide sequences of the probes are in the range of 10-200 nucleotides in length and are genomic sequences of a species of organism, such that a plurality of different probes is present, with sequences complementary and thus capable of hybridizing to the genome of such a species of organism, sequentially tiled across all or a portion of such genome. In other specific embodiments, the probes are in the range of 10-30 nucleotides in length, in the range of 10-40 nucleotides in length, in the range of 20-50 nucleotides in length, in the range of 40-80 nucleotides in length, in the range of 50-150 nucleotides in length, in the range of 80-120 nucleotides in length, and most preferably are 60 nucleotides in length.

The probes may comprise DNA or DNA “mimics” (e.g., derivatives and analogues) corresponding to a portion of an organism's genome. In another embodiment, the probes of the microarray are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits capable of specific, Watson-Crick-like hybridization with DNA, or of specific hybridization with RNA. The nucleic acids can be modified at the base moiety, at the sugar moiety, or at the phosphate backbone. Exemplary DNA mimics include, e.g., phosphorothioates.

DNA can be obtained, e.g., by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably chosen based on a known sequence of the genome that will result in amplification of specific fragments of genomic DNA. Computer programs that are well known in the art are useful in the design of primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically each probe on the microarray will be between 10 bases and 50,000 bases, usually between 300 bases and 1,000 bases in length. PCR methods are well known in the art, and are described, for example, in Innis et al., eds., PCR PROTOCOLS: A GUIDE TO METHODS AND APPLICATIONS, Academic Press Inc., San Diego, Calif. (1990). It will be apparent to one skilled in the art that controlled robotic systems are useful for isolating and amplifying nucleic acids.

An alternative, preferred means for generating the polynucleotide probes of the microarray is by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N-phosphonate or phosphoramidite chemistries (Froehler et al., Nucleic Acid Res. 14:5399-5407 (1986); McBride et al., Tetrahedron Lett. 24:246-248 (1983)). Synthetic sequences are typically between about 10 and about 500 bases in length, more typically between about 20 and about 100 bases, and most preferably between about 40 and about 70 bases in length. In some embodiments, synthetic nucleic acids include non-natural bases, such as, but by no means limited to, inosine. As noted above, nucleic acid analogues may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et al., Nature 363:566-568 (1993); U.S. Pat. No. 5,539,083).

Probes are preferably selected using an algorithm that takes into account binding energies, base composition, sequence complexity, cross-hybridization binding energies, and secondary structure. See Friend et al., International Patent Publication WO 01/05935, published Jan. 25, 2001; Hughes et al., Nat. Biotech. 19:342-7 (2001).

A skilled artisan will also appreciate that positive control probes, e.g., probes known to be complementary and hybridizable to sequences in the target polynucleotide molecules, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in the target polynucleotide molecules, should be included on the array. In one embodiment, positive controls are synthesized along the perimeter of the array. In another embodiment, positive controls are synthesized in diagonal stripes across the array. In still another embodiment, the reverse complement for each probe is synthesized next to the position of the probe to serve as a negative control. In yet another embodiment, sequences from other species of organism are used as negative controls or as “spike-in” controls.

The probes are attached to a solid support or surface, which may be made, e.g., from glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, or other porous or nonporous material. A preferred method for attaching the nucleic acids to a surface is by printing on glass plates, as is described generally by Schena et al, Science 270:467-470 (1995). This method is especially useful for preparing microarrays of cDNA (See also, DeRisi et al, Nature Genetics 14:457-460 (1996); Shalon et al., Genome Res. 6:639-645 (1996); and Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10539-11286 (1995)).

A second preferred method for making microarrays is by making high-density oligonucleotide arrays. Techniques are known for producing arrays containing thousands of oligonucleotides complementary to defined sequences, at defined locations on a surface using photolithographic techniques for synthesis in situ (see, Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and 5,510,270) or other methods for rapid synthesis and deposition of defined oligonucleotides (Blanchard et al., Biosensors & Bioelectronics 11:687-690). When these methods are used, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Usually, the array produced is redundant, with several oligonucleotide molecules per RNA.

Other methods for making microarrays, e.g., by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684), may also be used. In principle, and as noted supra, any type of array, for example, dot blots on a nylon hybridization membrane (see Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989)) could be used. However, as will be recognized by those skilled in the art, very small arrays will frequently be preferred because hybridization volumes will be smaller.

In one embodiment, the arrays of the present invention are prepared by synthesizing polynucleotide probes on a support. In such an embodiment, polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.

In a particularly preferred embodiment, microarrays of the invention are manufactured by means of an ink jet printing device for oligonucleotide synthesis, e.g., using the methods and systems described by Blanchard in U.S. Pat. No. 6,028,189; Blanchard et al., 1996, Biosensors and Bioelectronics 11:687-690; Blanchard, 1998, in Synthetic DNA Arrays in Genetic Engineering, Vol. 20, J. K. Setlow, Ed., Plenum Press, New York at pages 111-123. Specifically, the oligonucleotide probes in such microarrays are preferably synthesized in arrays, e.g., on a glass slide, by serially depositing individual nucleotide bases in “microdroplets” of a high surface tension solvent such as propylene carbonate. The microdroplets have small volumes (e.g., 100 pL or less, more preferably 50 μL or less) and are separated from each other on the microarray (e.g., by hydrophobic domains) to form circular surface tension wells, which define the locations of the array elements (i.e., the different probes). Microarrays manufactured by this ink-jet method are typically of high density, preferably having a density of at least about 2,500 different probes per 1 cm.sup.2. The polynucleotide probes are attached to the support covalently at either the 3′ or the 5′ end of the polynucleotide.

The polynucleotide molecules which may be analyzed by the present invention are DNA, RNA, or protein. The target polynucleotides are detectably labeled at one or more nucleotides. Any method known in the art may be used to detectably label the target polynucleotides. Preferably, this labeling incorporates the label uniformly along the length of the DNA or RNA, and more preferably, the labeling is carried out at a high degree of efficiency.

In a preferred embodiment, the detectable label is a luminescent label. For example, fluorescent labels, bioluminescent labels, chemiluminescent labels, and colorimetric labels may be used in the present invention. In a highly preferred embodiment, the label is a fluorescent label, such as a fluorescein, a phosphor, a rhodamine, or a polymethine dye derivative. Examples of commercially available fluorescent labels include, for example, fluorescent phosphoramidites such as FluorePrime (Amersham Pharmacia, Piscataway, N.J.), Fluoredite (Millipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and Cy3 or Cy5 (Amersham Pharmacia, Piscataway, N.J.). In another embodiment, the detectable label is a radiolabeled nucleotide.

In a further preferred embodiment, target polynucleotide molecules from a patient sample are labeled differentially from target polynucleotide molecules of a reference sample. The reference can comprise target polynucleotide molecules from normal tissue samples.

Nucleic acid hybridization and wash conditions are chosen so that the target polynucleotide molecules specifically bind or specifically hybridize to the complementary polynucleotide sequences of the array, preferably to a specific array site, wherein its complementary DNA is located.

Arrays containing double-stranded probe DNA situated thereon are preferably subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target polynucleotide molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target polynucleotide molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.

Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. One of skill in the art will appreciate that as the oligonucleotides become shorter, it may become necessary to adjust their length to achieve a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y. (1989), and in Ausubel et al., CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, vol. 2, Current Protocols Publishing, New York (1994). Typical hybridization conditions for the cDNA microarrays of Schena et al. are hybridization in 5×SSC plus 0.2% SDS at 65° C. for four hours, followed by washes at 25° C. in low stringency wash buffer (1×SSC plus 0.2% SDS), followed by 10 minutes at 25° C. in higher stringency wash buffer (0.1×SSC plus 0.2% SDS) (Schena et al., Proc. Natl. Acad. Sci. U.S.A. 93:10614 (1993)). Useful hybridization conditions are also provided in, e.g., Tijessen, 1993, HYBRIDIZATION WITH NUCLEIC ACID PROBES, Elsevier Science Publishers B.V.; and Kricka, 1992, NONISOTOPIC DNA PROBE TECHNIQUES, Academic Press, San Diego, Calif.

Particularly preferred hybridization conditions include hybridization at a temperature at or near the mean melting temperature of the probes (e.g., within 51° C., more preferably within 21° C.) in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine and 30% formamide.

When fluorescently labeled genetic regions or products of these genetic regions are used, the fluorescence emissions at each site of a microarray may be, preferably, detected by scanning confocal laser microscopy. In one embodiment, a separate scan, using the appropriate excitation line, is carried out for each of the two fluorophores used. Alternatively, a laser may be used that allows simultaneous specimen illumination at wavelengths specific to the two fluorophores and emissions from the two fluorophores can be analyzed simultaneously (see Shalon et al., 1996, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research 6:639-645, which is incorporated by reference in its entirety for all purposes). In a preferred embodiment, the arrays are scanned with a laser fluorescent scanner with a computer controlled X-Y stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line, mixed gas laser and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescence laser scanning devices are described in Schena et al., Genome Res. 6:639-645 (1996), and in other references cited herein. Alternatively, the fiber-optic bundle described by Ferguson et al., Nature Biotech. 14:1681-1684 (1996), may be used to monitor mRNA abundance levels at a large number of sites simultaneously.

Computer Systems

FIG. 15 illustrates a computer system 401 useful for implementing methodologies described herein. A system of the invention may include any one or any number of the components shown in FIG. 15. Generally, a system 401 may include a computer 433 and a server computer 409 capable of communication with one another over network 415. Additionally, data may optionally be obtained from a database 405 (e.g., local or remote). In some embodiments, systems include an instrument 455 for obtaining sequencing data, which may be coupled to a sequencer computer 451 for initial processing of sequence reads.

In some embodiments, methods are performed by parallel processing and server 409 includes a plurality of processors with a parallel architecture, i.e., a distributed network of processors and storage capable of collecting, filtering, processing, analyzing, ranking genetic data obtained through methods of the invention. The system may include a plurality of processors configured to, for example, 1) collect genetic data from different modalities: a) one or more infertility databases 405 (e.g. infertility databases, including private and public fertility-related data), b) from one or more sequencers 455 or sequencing computers 451, c) from mouse modeling, etc; 2) filter the genetic data to identify genetic variations; 3) associate genetic variations with infertility using methods described throughout the application (e.g., filtering, clustering, etc.); 4) determine statistical significance of genetic variations based on fertility criteria defined herein (e.g., Example 18); and 5) characterize/identify the genetic variations as infertility biomarkers.

By leveraging genetic data sets obtained across different sources, applying layers of analyses (i.e., filtering, clustering, etc.) to genetic data, and quantifying/qualifying statistical significance of that genetic data, systems of the invention are able yield and identify new infertility biomarkers that previously could not be determined to have any association with infertility. For example, methods of the invention utilize data sets from different modalities. The data sets range include data obtained from infertility databases (e.g., public and private), sequencing data (e.g., whole genome sequencing from one or more biological samples), and genetic data obtained from mouse modeling, etc. Several layers of analysis are then applied to the genetic data to identify whether variations are potentially associated with infertility. Particularly, the genetic data sets are subject to evolutionary conservation analysis, filtering analysis (see FIG. 5) and/or subject to clustering analysis (Example 20). After those analyses are applied, the variants potentially associated with infertilty are then assessed for biological and statistical significance. The variants that are determined to be statistically significant are then classified as infertility biomarkers, even if those variant had no prior association with infertility. Accordingly, using the invention's multi-modal and layered analysis, one is able to identify infertility biomarkers that would not have been identified or associated with infertility using standard techniques (i.e. comparing genetic sequences of an abnormal, infertile population to genetic sequences of a normal, fertile population).

While other hybrid configurations are possible, the main memory in a parallel computer is typically either shared between all processing elements in a single address space, or distributed, i.e., each processing element has its own local address space. (Distributed memory refers to the fact that the memory is logically distributed, but often implies that it is physically distributed as well.) Distributed shared memory and memory virtualization combine the two approaches, where the processing element has its own local memory and access to the memory on non-local processors. Accesses to local memory are typically faster than accesses to non-local memory.

Computer architectures in which each element of main memory can be accessed with equal latency and bandwidth are known as Uniform Memory Access (UMA) systems. Typically, that can be achieved only by a shared memory system, in which the memory is not physically distributed. A system that does not have this property is known as a Non-Uniform Memory Access (NUMA) architecture. Distributed memory systems have non-uniform memory access.

Processor-processor and processor-memory communication can be implemented in hardware in several ways, including via shared (either multiported or multiplexed) memory, a crossbar switch, a shared bus or an interconnect network of a myriad of topologies including star, ring, tree, hypercube, fat hypercube (a hypercube with more than one processor at a node), or n-dimensional mesh.

Parallel computers based on interconnected networks must incorporate routing to enable the passing of messages between nodes that are not directly connected. The medium used for communication between the processors is likely to be hierarchical in large multiprocessor machines. Such resources are commercially available for purchase for dedicated use, or these resources can be accessed via “the cloud,” e.g., Amazon Cloud Computing.

A computer generally includes a processor coupled to a memory and an input-output (I/O) mechanism via a bus. Memory can include RAM or ROM and preferably includes at least one tangible, non-transitory medium storing instructions executable to cause the system to perform functions described herein. As one skilled in the art would recognize as necessary or best-suited for performance of the methods of the invention, systems of the invention include one or more processors (e.g., a central processing unit (CPU), a graphics processing unit (GPU), etc.), computer-readable storage devices (e.g., main memory, static memory, etc.), or combinations thereof which communicate with each other via a bus.

A processor may be any suitable processor known in the art, such as the processor sold under the trademark XEON E7 by Intel (Santa Clara, Calif.) or the processor sold under the trademark OPTERON 6200 by AMD (Sunnyvale, Calif.).

Input/output devices according to the invention may include a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT) monitor), an alphanumeric input device (e.g., a keyboard), a cursor control device (e.g., a mouse or trackpad), a disk drive unit, a signal generation device (e.g., a speaker), a touchscreen, an accelerometer, a microphone, a cellular radio frequency antenna, and a network interface device, which can be, for example, a network interface card (NIC), Wi-Fi card, or cellular modem.

INCORPORATION BY REFERENCE

References and citations to other documents, such as patents, patent applications, patent publications, journals, books, papers, web contents, have been made throughout this disclosure. All such documents are hereby incorporated herein by reference in their entirety for all purposes.

EQUIVALENTS

The invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting on the invention described herein. Scope of the invention is thus indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

EXAMPLES Example 1—Identification of Oocyte Proteins

Oocytes are collected from females, for example mice, by superovulation, and zona pellucidae are removed by treatment with acid Tyrode solution. Oocyte plasma membrane (oolemma) proteins exposed on the surface can be distinguished at this point by biotin labeling. The treated oocytes are washed in 0.01 M PBS and treated with lysis buffer (7 M urea, 2 M thiourea, 4% (w/v) 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), 65 mM dithiothreitol (DTT), and 1% (v/v) protease inhibitor at −80° C.). Oocyte proteins are resolved by one-dimensional or two-dimensional SDS-PAGE. The gels are stained, visualized, and sliced. Proteins in the gel pieces are digested (12.5 ng/μl trypsin in 50 mM ammonium bicarbonate overnight at 37° C.), and the peptides are extracted and microsequenced.

Example 2—Sample Population for Identification of Infertility-Related Polymorphisms

Genomic DNA is collected from 30 female subjects (15 who have failed multiple rounds of IVF versus 15 who were successful). In particular, all of the subjects are under age 38. Members of the control group succeeded in conceiving through IVF. Members of the test group have a clinical diagnosis of idiopathic infertility, and have failed three of more rounds of IVF with no prior pregnancy. The women are able to produce eggs for IVF and have a reproductively normal male partner. To focus on infertility resulting from oocyte defects (and eliminate factors such as implantation defects) women who have subsequently conceived by egg donation are favored.

Example 3—Sample Population for Identification of Infertility-Related Polymorphisms

In a follow-up study of a larger cohort, genomic DNA is collected from 300 female subjects (divided into groups having profiles similar to the groups described above). The DNA sequence polymorphisms to be investigated are selected based on the results of small initial studies.

Example 4—Sample Population for Identification of Premature Ovarian Failure (POF) and Premature Maternal Aging Polymorphisms

Genomic DNA is collected from 30 female subjects who are experiencing symptoms of premature decline in egg quality and reserve including abnormal menstrual cycles or amenorrhea. In particular, all of the subjects are between the ages of 15-40 and have follicle stimulating hormone (FSH) levels of over 20 international units (IU) and a basal antral follicle count of under 5. Members of the control group succeeded in conceiving through IVF. Members of the test group have no previous history of toxic exposure to known fertility damaging treatments such as chemotherapy. Members of this group may also have one or more female family member who experienced menopause before the age of 40.

Example 5—Sample Procurement and Preparation

Blood is drawn from patients at fertility clinics for standard procedures such as gauging hormone levels and many clinics bank this material after consent for future research projects. Although DNA is easily obtained from blood, wider population sampling is accomplished using home-based, noninvasive methods of DNA collection such as saliva using an Oragene DNA self collection kit (DNA Genotek).

Blood samples—Three-milliliter whole blood samples are venously collected and treated with sodium citrate anticoagulant and stored at 4° C. until DNA extraction.

Whole Saliva—Whole saliva is collected using the Oragene DNA selfcollection kit following the manufacturer's instructions. Participants are asked to rub their tongues around the inside of their mouths for about 15 sec and then deposit approximately 2 ml saliva into the collection cup. The collection cup is designed so that the solution from the vial.'s lower compartment is released and mixes with the saliva when the cap is securely fastened. This starts the initial phase of DNA isolation, and stabilizes the saliva sample for long-term storage at room temperature or in low temperature freezers. Whole saliva samples are stored and shipped, if necessary, at room temperature. Whole saliva has the potential advantage over other non-invasive DNA sampling methods, such as buccal and oral rinse, of providing large numbers of nucleated cells (eg., epithelial cells, leukocytes) per sample.

Blood clots—Clotted blood that is usually discarded after extraction through serum separation, for other laboratory tests such as for monitoring reproductive hormone levels is collected and stored at −80° C. until extraction.

Sample Preparation—Genomic DNA is prepared from patient blood or saliva for downstream sequencing applications with commercially available kits (e.g., Invitrogen.'s ChargeSwitch® gDNA Blood Kit or DNA Genotek kits, respectively). Genomic DNA from clotted is prepared by standard methods involving proteinase K digestion, salt/chloroform extraction and 90% ethanol precipitation of DNA. (see N Kanai et al., 1994, “Rapid and simple method for preparation of genomic DNA from easily obtainable clotted blood,” J Clin Pathol 47:1043-1044, which is incorporated by reference in its entirety for all purposes).

Example 6—Manufacturing of a Customized Oligonucleotide Library

A customized oligonucleotide library can be used to enrich samples for DNAs of interest. Several methods for manufacturing customized oligonucleotide libraries are known in the art. In one example, Nimblegen sequence capture custom array design is used to create a customized target enrichment system tailored to infertility related genes. A customized library of oligonucleotides is designed to target genetic regions of Tables 1-7. The custom DNA oligonucleotides are synthesized on a high density DNA Nimblegen Sequence Capture Array with Maskless Array Synthesizer (MAS) technology. The Nimblegen Sequence Capture Array system workflow is array based and is performed on glass slides with an X1 mixer (Roche NimbleGen) and the NimbleGen Hybridization System.

In a similar example, Agilent's eArray (a web-based design tool) is used to create a customized target enrichment system tailored to infertility related genes. The SureSelect Target Enrichment System workflow is solution-based and is performed in microcentrifuge tubes or microtiter plates. A customized oligonucleotide library is used to enrich samples for DNA of interest. Agilent's eArray (a web-based design tool) is used to create a customized target enrichment system tailored to infertility related genes. A customized library is designed to target genetic regions of Tables 1-7. The custom RNA oligonucleotides, or baits, are biotinylated for easy capture onto streptavidin-labeled magnetic beads and used in Agilent's SureSelectTarget Enrichment System. The SureSelect Target Enrichment System workflow is solution-based and is performed in microcentrifuge tubes or microtiter plates.

Example 7—Capture of Genomic DNA

Genomic DNA is sheared and assembled into a library format specific to the sequencing instrument utilized downstream. Size selection is performed on the sheared DNA and confirmed by electrophoresis or other size detection method.

Several methods to capture genomic DNA are known in the art. In one example, the size-selected DNA is purified and the ends are ligated to annealed oligonucleotide linkers from Illumina to prepare a DNA library. DNA-adaptor ligated fragments are hybrized to a Nimblegen Sequence Capture array using an X1 mixer (Roche NimbleGen) and the Roche NimbleGen Hybridization System. After hybridization, are washed and DNA fragments bound to the array are eluted with elution buffer. The captured DNA is then dried by centrifugation, rehydrated and PCR amplified with polymerase. Enrichment of DNA can be assessed by quantitative PCR comparison to the same sample prior to hybridization.

In a similar example, the size-selected DNA is incubated with biotinylated RNA oligonucleotides “baits” for 24 hours. The RNA/DNA hybrids are immobilized to streptavidin-labeled magnetic beads, which are captured magnetically. The RNA baits are then digested, leaving only the target selected DNA of interest, which is then amplified and sequenced.

Example 8—Sequencing of Target Selected DNA

Target-selected DNA is sequenced by a paired end (50 bp) re-sequencing procedure using Illumina.'s Genome Analyzer. The combined DNS targeting and resequencing provides 45 fold redundancy which is greater than the accepted industry standard for SNP discovery.

Example 9—Correlation of Polymorphisms with Fertility

Polymorphisms among the sequences of target selected DNA from the pool of test subjects are identified, and may be classified according to where they occur in promoters, splice sites, or coding regions of a gene. Polymorphisms can also occur in regions that have no apparent function, such as introns and upstream or downstream non-coding regions. Although such polymorphisms may not be informative as to the functional defect of an allele, nevertheless, they are linked to the defect and useful for predicting infertility. The polymorphisms are analyzed statistically to determine their correlation with the fertility status of the test subjects. The statistical analysis indicates that certain polymorphisms identify gene defects that by themselves (homozygous or heterozygous) are sufficient to cause infertility. Other polymorphisms identify genetic variants that reduce, but do not eliminate fertility. Other polymorphisms identify genetic variants that have an apparent effect on fertility only in the presence of particular variants of other genes. Other polymorphisms identify genetic variants that have an apparent effect on fertility only in the presence of particular phenotypes. Other polymorphisms identify genetic variants that have an apparent effect on fertility only in the presence of particular environmental exposures. Still other polymorphisms identify genetic variants that have an apparent effect on fertility only in the presence of any combination of particular variants of other genes, presence of particular phenotypes, and particular environmental exposures.

Example 10—Correlation of Polymorphisms with Premature Ovarian Failure (POF)

Polymorphisms among the sequences of target selected DNA from the pool of test subjects are identified, and may be classified according to where they occur in promoters, splice sites, or coding regions of a gene. Polymorphisms can also occur in regions that have no apparent function, such as introns and upstream or downstream non-coding regions. Although such polymorphisms may not be informative as to the functional defect of an allele, nevertheless, they are linked to the defect and useful for predicting likelihood of premature ovarian failure (POF). The polymorphisms are analyzed statistically to determine their correlation with the POF status of the test subjects. The statistical analysis indicates that certain polymorphisms identify gene defects that by themselves (homozygous or heterozygous) are sufficient to cause POF. Other polymorphisms identify genetic variants that increase the likelihood, but do not cause POF. Other polymorphisms identify genetic variants that have an apparent effect on POF only in the presence of particular variants of other genes. Other polymorphisms identify genetic variants that have an apparent effect on POF only in the presence of particular phenotypes. Other polymorphisms identify genetic variants that have an apparent effect on POF only in the presence of particular environmental exposures. Still other polymorphisms identify genetic variants that have an apparent effect on POF only in the presence of any combination of particular variants of other genes, presence of particular phenotypes, and particular environmental exposures.

Example 11—Correlation of Polymorphisms with Premature Maternal Aging

Polymorphisms among the sequences of target selected DNA from the pool of test subjects are identified, and may be classified according to where they occur in promoters, splice sites, or coding regions of a gene. Polymorphisms can also occur in regions that have no apparent function, such as introns and upstream or downstream non-coding regions. Although such polymorphisms may not be informative as to the functional defect of an allele, nevertheless, they are linked to the defect and useful for predicting likelihood of premature decline in ovarian reserve and egg quality (i.e. maternal aging). The polymorphisms are analyzed statistically to determine their correlation with the maternal aging status of the test subjects. The statistical analysis indicates that certain polymorphisms identify gene defects that by themselves (homozygous or heterozygous) are sufficient to cause premature maternal aging. Other polymorphisms identify genetic variants that increase the likelihood, but do not cause premature maternal aging. Other polymorphisms identify genetic variants that have an apparent effect on premature maternal aging only in the presence of particular variants of other genes. Other polymorphisms identify genetic variants that have an apparent effect on premature maternal aging only in the presence of particular phenotypes. Other polymorphisms identify genetic variants that have an apparent effect on premature maternal aging only in the presence of particular environmental exposures. Still other polymorphisms identify genetic variants that have an apparent effect on premature maternal aging only in the presence of any combination of particular variants of other genes, presence of particular phenotypes, and particular environmental exposures.

Example 12—Diagnostics and Counseling

A library of nucleic acids in an array format is provided for infertility diagnosis. The library consists of selected nucleic acids for enrichment of genetic targets wherein polymorphisms in the targets are correlated with variations in fertility. A patient nucleic acid sample (appropriately cleaved and size selected) is applied to the array, and patient nucleic acids that are not immobilized are washed away. The immobilized nucleic acids of interest are then eluted and sequenced to detect polymorphisms. According to the polymorphisms detected, the fertility status of the patient is evaluated and/or quantified. The patient is accordingly advised as to the suitability and likelihood of success of a fertility treatment or suitability or necessity of a particular in vitro fertilization procedure.

Example 13—Diagnostics and Counseling

A complete DNA sequence of any number of or all of the genes in Tables 1-7 is determined using a targeted resequencing protocol. According to the polymorphisms detected and the phenotypic traits and environmental exposures reported, the fertility status of the patient is evaluated and/or quantified. The patient is accordingly advised as to the suitability and likelihood of success of a fertility treatment or suitability or necessity of a particular in vitro fertilization procedure.

Example 14—Diagnostics and Counseling

A library of nucleic acids in an array format is provided for infertility diagnosis. The library consists of selected nucleic acids for enrichment of genetic targets wherein polymorphisms in the targets are correlated with variations in fertility. A patient nucleic acid sample (appropriately cleaved and size selected) is applied to the array, and patient nucleic acids that are not immobilized are washed away. The immobilized nucleic acids of interest are then eluted and sequenced to detect polymorphisms. According to the polymorphisms detected and the phenotypic traits and environmental exposures reported, the POF status of the patient or likelihood of future POF occurrence is evaluated and/or quantified. The patient is accordingly advised as to whether preventative egg or ovary preservation is indicated.

Example 15—Diagnostics and Counseling

A complete DNA sequence of any number of or all of the genes in Tables 1-7 is determined using a targeted resequencing protocol. According to the polymorphisms detected and the phenotype and environmental exposures reported, the fertility status of the patient is evaluated and/or quantified. According to the polymorphisms detected and the phenotypic traits and environmental exposures reported, the POF status of the patient or likelihood of future POF occurrence is evaluated and/or quantified. The patient is accordingly advised as to whether preventative egg or ovary preservation is indicated.

Example 16—Diagnostics and Counseling

A library of nucleic acids in an array format is provided for infertility diagnosis. The library consists of selected nucleic acids for enrichment of genetic targets wherein polymorphisms in the targets are correlated with variations in fertility. A patient nucleic acid sample (appropriately cleaved and size selected) is applied to the array, and patient nucleic acids that are not immobilized are washed away. The immobilized nucleic acids of interest are then eluted and sequenced to detect polymorphisms. According to the polymorphisms detected and the phenotypic traits and environmental exposures reported, the maternal aging status of the patient or likelihood of future premature maternal aging occurrence is evaluated and/or quantified. The patient is accordingly advised as to whether preventative egg or ovary preservation, minimization of certain environmental exposures such as alcohol intake or smoking, or mitigation of certain phenotypes such as having children at a younger age is indicated.

Example 17—Diagnostics and Counseling

A complete DNA sequence of any number of or all of the genes in Tables 1-7 is determined using a targeted resequencing protocol. According to the polymorphisms detected and the phenotypic traits and environmental exposures reported, the fertility status of the patient is evaluated and/or quantified. According to the polymorphisms detected and the phenotype and environmental exposures reported, the maternal aging status of the patient or likelihood of future premature maternal aging occurrence is evaluated and/or quantified. The patient is accordingly advised as to whether preventative egg or ovary preservation, minimization of certain environmental exposures such as alcohol intake or smoking, or mitigation of certain phenotypes such as having children at a younger age is indicated.

Example 18—Whole Genome Sequencing for Female Infertility Biomarker Discovery

Whole genome sequencing (WGS) allows one to characterize the complete nucleic acid sequence of an individual's genome. With the amount of data obtained from WGS, a comprehensive collection of an individual's genetic variation is obtainable, which provides great potential for genetic biomarker discovery. The data obtained from WGS can be advantageously used to expand the ability to identify and characterize female infertility biomarkers. However, the ability to identify unknown variations of fertility significance within the vast WGS datasets is a challenging task that is analogous to finding a needle in a haystack.

Methods of the invention, according to certain embodiments, rely on bioinformatics to filter through WGS data in order to identify and prioritize variations of infertility significance. Specifically, the invention relies on a combination of clinical phenotypic data and an infertility knowledgebase to rank and/or score genomic regions of interest and their likely impact on different fertility disorders. In certain aspects, the filtering approach involves assessing sequencing data to identify genomic variations, identifying at least one of the variations as being in a genomic region associated with infertility, determining whether the at least one variation is a biologically-significant variation and/or a statistically-significant variation, and characterizing at least one identified variation as an infertility biomarker based on the determining step. A genomic region associated with infertility is any DNA sequence in which variation is associated with a change in fertility. Such regions may include genes (e.g. any region of DNA encoding a functional product), genetic regions (e.g. regions including genes and intergenic regions with a particular focus on regions conserved throughout evolution in placental mammals), and gene products (e.g., RNA and protein). In particular embodiments, the infertility-associated genetic region is a maternal effect gene, as described above. In particular embodiments, the infertility-associated genetic region is a gene (including exons, introns, and evolutionarily conserved regions of DNA flanking either side of said gene) that impacts fertility.

This filtering approach facilitates rapid identification of functionally relevant variants within genomic regions of significance for fertility. The identified variations with infertility significance obtained from WGS data may be used in diagnostic testing, and ultimately assist physicians in data interpretation, guide fertility therapeutics, and clarify why some patients are not responding to treatment. The following illustrates use of WGS data to identify variants of interest in accordance with methods of the invention.

FIG. 5 generally illustrates filtering through variations obtained from WGS sequencing data in order to identify variations of infertility significance. As shown in FIG. 5, the first step is to identify sequence variants in whole genome sequence. A typical whole genome can include up to four million variants. The next filtering step involves eliminating variants outside of regions of interest for female fertility (which amounts to about one million variants). Next, the filtering method isolates variants within regions of interest for female fertility, which is described herein as Fertilome nucleic acid (i.e. regions of the human genome that control egg quality and fertility). Variations located within the Fertilome nucleic acid may be in the 100,000s. The variations within the Fertilome nucleic acid are further filtered to identify and score variations of infertility significance (such variations are typically present in double digits). Particularly, variations of infertility significance include those within regions predicted to effect biological function or that show a statistical correlation to infertility or treatment failure.

Biologically-significant variations within the Fertilome nucleic acid include mutations that result in a change: 1) to a different amino acid predicted to alter the folding and/or structure of the encoded protein, 2) to a different amino acid occurring at a site with high evolutionarily conservation in mammals, 3) that introduces a premature stop termination signal, 4) that causes a stop termination signal to be lost, 5) that introduces a new start codon, 6) that causes a start codon to be lost or 7) that disrupts a splicing signal. Statistically-significant variations within the Fertilome nucleic acid are described in relation to and listed in Tables 2 and 3. Other methods for classifying variations as statistically- or biologically-significant includes scoring variations using an infertility knowledgebase (which is described in relation to Tables 5-7 above and FIG. 6 below). The infertility knowledgebase ranks genes based on attributes associated with infertility. The attributes include: diseases and disorders related to infertility, molecular pathways, molecular interactions, gene clusters, mouse phenotypes associated with each gene, gene expression data in reproductive tissues, proteomics data in oocytes, and accrued information from scientific publications through text-mining. List of ranked genes of interest are provided in Tables 5-7.

FIG. 6 illustrates various data sources integrated into the infertility knowledgebase for analyzing whole-genome sequencing data according to certain embodiments. As shown in FIG. 6, information is obtained from private and public fertility-related data. Private and/or public fertility-related data may include implantation genes, idiopathic infertility genes, polycystic ovary syndrome (PCOS) genes, egg quality genes, endometriosis genes, and premature ovarian failure genes. The private and/or public fertility-related data is then subjected to the ABCoRE Algorithm to provide genomic regions and variations of interest that can be introduced into a fertility database evidence matrix along with other fertility-related information. As described in the detailed description, the ABCoRE algorithm identifies fertility regions of interest by performing evolutionary conservation analysis of one or more genes obtained from the private and/or public fertility-related data. The other fertility-related information includes, for example, protein-protein interactions, pathway interactions, gene orthologs and paralogs, genomic “hotpsots”, gene protein expression and meta-analysis, and data from genomic studies. In operation, whole genomic sequencing data is compared to the compiled data in the fertility database evidence matrix to facilitate identification of potential genetic regions important for fertility. The fertility database evidence matrix filters through WGS variants to identify variants of fertility significance. In certain embodiments, the whole genomic sequencing data is also subjected to the SESMe algorithm that ranks each genetic region from most to least important for different aspects of female fertility.

FIG. 7 illustrates a bioinformatics pipeline used to filter through WGS data to identify biomarkers associated with infertility according to certain embodiments. As shown in FIG. 7, samples are subjected to whole genome sequencing, mapping, and assembly. The WGS data is then analyzed to discover genetic variants such as SNPs, small indels, mobile elements, copy number variations, and structural variations. The identified variations are then assessed for statistical significance (See, for example, Tables 2 and 3 above). This includes correction for population stratification, variation-level significance tests, and gene level significance tests. In addition, the biological significance of WGS variants is determined using the SnpEff and Variant Effect Predictor (www.ensembl.org) engines (See, for example, Table 1 above). Variants of biological and statistical significance are then entered into the infertility knowledgebase (i.e. Fertilome database) in order to classify those variants as fertility biomarkers.

The following illustrates use of WGS data to identify variants of interest in accordance with methods of the invention.

Samples were collected from female patients undergoing fertility treatment at an academic reproductive medical center, and categorized into idiopathic infertility or primary ovarian insufficiency (POI) study groups. Phenotypic information was collected for each patient by mining >200 variables from electronic health records. Genomic DNA extracted from blood samples underwent WGS by Complete Genomics (Mountain View, Calif.). Analysis of genetic variants from WGS was assisted by an infertility knowledgebase with >800 genomic regions of interest (ROI) ranked by a scoring algorithm predicting their likely impact on different fertility disorders, based on publications, data repositories (including protein-protein interactions and tissue expression patterns), meta-analyses of these data, and animal model phenotypes.

The collected female samples were subjected to the processes/algorithms depicted in FIGS. 5-7 (described in more detail above). With those female samples, approximately 50,000 novel variants (approximately 1.6% of total variants observed) were identified as having fertility significances that have not been previously reported in databases such as the sbSNP reference. The identified fertility-related variants included single nucleotide polymorphisms (SNPs, insertions, deletions, copy number variations, inversions, and translocations. Of the SNPs, some of them are predictive to have putative functional significance based on the knowledgebase. For example, the knowledgebase scored some SNPs as deleterious mutations due to potential loss of function or changes in protein structure.

In certain aspects, the genomic data, such as WGS data, of a patient/subject population is subjected to a population stratification correction. Population stratification correction accounts for the presence of a systematic difference in allele frequencies between subpopulations in a population possibly due to different ancestry. When conducting population stratification, data is compared to a number (e.g. 1,000) of ethnically diverse individuals as part of the 1000 Genomes Project (100G). Principal components analysis (PCA) is applied to model and identify ancestry differences. In addition, computed association statistics are adjusted for the first two principal components.

FIG. 13 illustrates population stratification correction of two patient groups. The patient groups include female patients undergoing non-donor in vitro fertilization (IVF) cycles. The patients were 38 years old or younger at the time of enrollment, and had no history of carrying a pregnancy beyond the first term before IVF treatment. Each patient had lack of an apparent cause for infertility (i.e. unexplained) after an evaluation of a complete medical history, physical examination, endocrine profile, and the results of an intimate partner's sperm analysis. The patients were divided into two groups. Group A included 11 patients that experienced no live birth or pregnancy beyond the first trimester after 3 or more IVF cycles. Group B included 18 patients that experienced live birth or pregnancy beyond the first trimester through use of IVF therapy. With population stratification correction, Group A and B patients cluster (are shown as black dots) with East Asian, African, Hispanic, and European individuals as shown in the principal component analysis chart of FIG. 13. This data shows that ethnicity may be linked to infertility, or that certain genomic variations are more prevalent in certain ethnic populations. Accordingly, aspects of the invention involve assessing ethnicity of an individual, either through self-reporting by the individual (e.g., by a questionnaire) or via an assay that looks for known biomarkers related to genetic ethnicity of an individual. That ethnicity data (genetic or self-reported) may be used to guide testing, such as by ensuring that certain genomic variations are checked that are known to be associated with certain ethnic populations.

Example 19

Approximately 15% of couples experiencing difficulty conceiving are diagnosed with idiopathic infertility. Genetic polymorphisms could shed light on many of these currently unexplained cases by revealing disruptions to oocyte quality or uterine receptivity that may exist on a subcellular level.

In accordance with certain aspects, copy number variations are examined for their effect on female fertility using comparative genomic hybridization (CGH) arrays. CGH provides for methods of determining the relative number of copies of nucleic acid sequences in one or more subject genomes or portions thereof (for example, an infertility marker) as a function of the location of those sequences in a reference genome (for example, a normal human genome). As a result, CGH provides a map of losses and gains in nucleic acid copy number across the entire genome without prior knowledge of specific chromosomal abnormalities. Methods of the invention capitalize on the ability to detect copy number variations without the need for prior knowledge in order to detect potential mutations with infertility significance within patient populations that have unexplained infertility.

The following illustrates use of CGH arrays to identify copy number variants of interest in accordance with methods of the invention.

The study examined female patients undergoing non-donor in vitro fertilization (IVF) cycles. The patients were 38 years old or younger at the time of enrollment, and had no history of carrying a pregnancy beyond the first term before IVF treatment. Each patient had lack of an apparent cause for infertility (i.e. unexplained) after an evaluation of a complete medical history, physical examination, endocrine profile, and the results of an intimate partner's sperm analysis. The patients were divided into two groups. Group A included 11 patients that experienced no live birth or pregnancy beyond the first trimester after 3 or more IVF cycles. Group B included 18 patients that experienced live birth or pregnancy beyond the first trimester through use of IVF therapy.

FIG. 9 provides CGH array data of copy number variations detected in the study populations within statistically significant regions associated with infertility (i.e. copy number variations within the Fertilome nucleic acid). FIG. 10 illustrates a specific copy number variation detected in the GJC2 gene of Chromosome 1 within Groups A and B. This region is specifically expressed in both the oocyte and brain, and is known to be associated with embryo issues. As shown, the region within GJC2 showed deletion in the most infertile patients. FIG. 11 illustrates a specific copy number variation detected in the CRTC1 and GDF1 genes of Chromosome 19 within Groups A and B. CRTC1 is associated with ovary, oocyte, endometrium, and placenta expression. GDF1 is associated with defects in the formation of anterior visceral endoderm and mesoderm. As shown, both patient groups exhibit copy number deletions in those genes. FIG. 12 illustrates a specific copy number variation detected in a non-coding region of Chromosome 6. As shown, both patient groups exhibit copy number duplication that region.

Example 20

In addition to using the existing infertility knowledge base to identify new genetic variations associated with infertility (e.g., Example 18), methods of the invention further utilize the existing infertility knowledgebase to identify commonalities between known infertility genes and genes having no prior association with infertility. By identifying commonalities between infertility genes and genes having no prior association with infertility, one is able to expand the list of potential genes associated with infertility and guide understanding as to what gene functions and changes are causally-linked to infertility. For example, genes having commonalities with known infertility genes can be identified as potential infertility biomarkers, and used in phenotypic studies (such those performed in mice) related to infertility, thereby expanding the breadth infertility knowledgebase.

In order to determine commonalities between infertility genes and genes without prior associated with infertility, methods of the invention utilize cluster analysis techniques. Generally, a cluster analysis involves grouping a set of objects in such a way that certain objects are clustered in one group are more similar to each other than objects in another group or cluster. Methods of the invention cluster known infertility genes with genes not associated with infertility based on features such as gene expression, phenotype, and genetic pathways. From the cluster analysis, one can identify genes without prior association with infertility that exhibit features with a high degree of similarity (relatedness) to infertility genes. Those genes exhibiting a high degree of similarity (as shown through the cluster analysis) can be identified as a potential infertility biomarker.

The following describes a clustering method used to identify a potential infertility biomarker in accordance with methods of the invention. The method is typically a computer-implemented method, e.g. utilizes a computer system that includes a processor and a computer readable storage medium. The processor of the computer system executes instructions obtained from the computer-readable storage device to perform the cluster analysis.

In accordance with to certain aspects, the method involves obtaining a gene data set that includes both known infertility genes and genes having no prior association with infertility. In certain embodiments, the gene data sets may be taken from known infertility databases, sequencing data obtained from patients, or sequencing data obtained from mouse modeling studies. The genes forming the cluster data set (those associated with infertility and those not known to be associated with infertility) are typically mammalian genes. The mammalian genes may correspond to mouse genes, human, genes, or a combination thereof. A cluster analysis is then performed on the gene data set to determine a relationship between the one or more genes not associated with infertility and the known infertility genes. If a gene not associated with infertility is shown to cluster with a known infertility gene, the method provides for identifying that gene as a potential infertility biomarker. If the gene not associated with infertility does not cluster with a known infertility gene, then that gene is less likely to be causally linked to infertility in the same/similar manner as that known infertility gene.

Methods of the invention assess several features (or parameters) of genes in order to determine commonalities and thus cluster genes not associated with infertility with known infertility genes based on the commonalities. In certain embodiments, those features include gene expression, phenotypes, gene pathways, and a combination thereof. One or more of those features can contribute to a gene's position in the clustering.

Feature data (such as gene expression, phenotype, gene pathway, etc.) is obtained for both known infertility genes and genes not known to be associated with infertility. The feature and gene data is compiled to form a matrix that will be used to exhibit the cluster analysis. For example, the feature data is pre-processed to express each domain as a row and each feature as a column (or vice versa). For domains with continuous values such as gene expression, the features are the individual tissues where gene expression was measured, and each value in the matrix (Xij) represents the expression of gene i in tissue j. For domains with categorical values such as phenotypes, the features are the individual phenotypes, and each value in the matrix (Xij) is a binary indicator representing whether gene i is associated with phenotype j. All of the domain specific matrices are then combined column-wise. A distance metric is then applied to each pair of rows and each pair of columns in the matrix. In certain embodiments, the distance metric is ‘Distance=1-correlation’. However, it is understood that other standard distance metrics could be used (e.g. Euclidean).

Standard hierarchical clustering is then used to cluster the rows and columns of the matrix in order to determine feature commonalities between known infertility genes and other genes. Various hierarchical clustering techniques are known in the art, and can be applied to methods of the invention for clustering infertility genes with genes not associated with infertility. Hierarchical clustering techniques are described in, for example, Sturn, Alexander, John Quackenbush, and Zlatko Trajanoski. “Genesis: cluster analysis of microarray data.” Bioinformatics 18.1 (2002): 207-208; Yeung, Ka Yee, and Walter L. Ruzzo. “Principal component analysis for clustering gene expression data.” Bioinformatics 17.9 (2001): 763-774; Eisen, Michael B., et al. “Cluster analysis and display of genome-wide expression patterns.” Proceedings of the National Academy of Sciences 95.25 (1998): 14863-14868. Generally, clustering involves comparing features of one or more genes not associated with features of one or more known infertility, and categorizing the genes into one or more feature groups based on the comparison. After the comparison, the cluster analysis may further involve assigning a value to the categorized genes based on a degree of relatedness. For example, genes clustered together having highly similar or the same features may be assigned a high value (e.g. positive integer). The degree of relatedness may be highlighted on the resulting cluster matrix via colors, e.g. high degree of commonality being shown in red and low degree of commonality being shown in blue.

After a hierarchical clustering technique is applied to the gene/feature data, the gene clusters are displayed against certain feature categories (e.g. phenotype/gene expression ‘category’), which are then clustered to reflect commonality. For example, phenotypes of female reproduction are grouped together in one cluster, and phenotypes of embryo patterning, morphology and growth are grouped in a separate cluster, etc. The degree of relatedness or commonality between clustered genes (as determined by the cluster analysis) can then be highlighted on the resulting cluster matrix. For example, red may be used to indicate that the gene is associated with one very specific phenotype and/or is expressed at high levels in the associated tissue/physiological system indicated on the opposite axis; whereas blue may be used to indicate that the gene is associated with a number of different and varied phenotypes and/or is expressed at low levels in the associated tissue.

By clustering genes into feature specific groups and color-coding genes with high degree of relatedness, the resulting cluster matrix of the invention advantageously allows for visualization of groups of genes that are strongly associated with phenotypes relating to particular tissues or physiological systems (i.e. clusters of interest). Thus, cluster matrices of the invention allow one to quickly identify genes without prior association with infertility as potential infertility biomarkers based on their shown association (cluster) with known infertility biomarkers. This clustering and identification of potential infertility biomarkers is done independently from and without correlating a gene's proximity with other genes within or location on the Fertilome (genomic region associated with infertility). As a result, clustering provides an additional method of identifying infertility genes of interest that can be used to complement and in addition to other techniques for identifying infertility genes of interest.

The following describes a specific example of using the above described cluster analysis to correlate genes not known to be associated with infertility and a known infertility gene.

Activin receptor 2b (ACVR2B) is a significant copy number variation identified in a cohort of patients with infertility (i.e. copy number variation in this gene was identified as being significantly associated with an infertile phenotype in humans). Activin receptor 2B is the receptor bound by Activin, a protein previously known in the art to be involved in both human and mouse reproduction and embryonic development. Activin/Nodal signaling regulates pluripotency and several aspects of patterning during early embryogenesis. Together with Inhibin and Follistatin, Activin is also involved in the complex feedback loops that selectively regulate FSH secretion.

A cluster analysis was performed that compared those features of ACVR2B and features of a plurality of genes not known to be associated with infertility. Based on the cluster analysis, several of the plurality of genes were determined to cluster with the ACVR2B gene due to a commonality between functional and phenotypic features. The genes clustered with the ACVR2B gene were thus identified as potential infertility biomarkers. FIG. 14 illustrates the results of a cluster analysis with ACVR2B. 

What is claimed is:
 1. A system for identifying a potential infertility biomarker, the system comprising: a processor; and a computer-readable storage device containing instructions that when executed by the processor cause the system to: receive sequencing data from one or more sequencers communicatively coupled to processor, the sequencing data obtained by assaying a biological sample from a female having a clinical diagnosis of idiopathic infertility; receive data on a set of genes from one or more infertility databases communicatively coupled to the processor, wherein the sequencing data and the data form multi-modal data comprising genes known to be associated with infertility and genes having no prior association with infertility, and wherein the multi-modal data comprises one or more features for each gene selected from the group consisting of a phenotype, a gene expression pattern, a genetic pathway, and a combination thereof; perform a cluster analysis on the multi-modal data to identify one or more of the genes having no prior association with infertility that cluster with one or more of the genes known to be associated with infertility, the cluster analysis based on feature commonalities between the genes that have no prior association and the genes known to be associated with infertility; and provide an output via a display unit that identifies at least one of the genes having no prior association with infertility as a potential infertility biomarker based on it clustering with one or more genes known to be associated with infertility.
 2. The system of claim 1, wherein the cluster analysis comprises the steps of identifying at least one feature of one or more of the genes known to be associated with infertility; analyzing one or more of the genes having no prior association with infertility for the corresponding feature; and comparing the at least one feature of the genes known to be associated with infertility with the corresponding feature of the genes having no prior association with infertility to identify feature commonalities.
 3. The system of claim 2, wherein the cluster analysis further comprises categorizing the one or more genes having no prior association with infertility into one or more feature groups based on the comparison.
 4. The system of claim 3, wherein the cluster analysis further comprises assigning a value to the categorized genes based on a degree of relatedness.
 5. The system of claim 1, wherein the one or more genes having no prior association with infertility are mammalian genes.
 6. The system of claim 1, wherein the mammalian genes correspond to a species selected from mouse, human, and a combination thereof.
 7. The system of claim 1, wherein the genes known to be associated with infertility are mammalian genes.
 8. The system of claim 7, wherein the mammalian genes correspond to a species selected from mouse, human, and a combination thereof.
 9. The system of claim 1, wherein the cluster analysis comprises: compiling the multi-modal data to form a matrix that will be used to exhibit the cluster analysis, wherein each domain is expressed as a row and each feature is expressed as a column; applying a distance metric to each pair of rows and each pair of columns in the matrix; clustering, using hierarchical clustering, the rows and columns of the matrix in order to determine feature commonalities between the genes known to be associated with infertility and the genes having no prior association with infertility.
 10. A computer-implemented method for identifying a potential infertility biomarker, the method comprising: assaying a biological sample from a female having a clinical diagnosis of idiopathic infertility to obtain sequencing data; receiving to a computer, multi-modal data on a set of genes including data obtained from at least one infertility-associated database and the sequencing data, wherein the set comprising genes known to be associated with infertility and genes having no prior association with infertility and wherein the multi-modal data comprises one or more features for each gene selected from the group consisting of a phenotype, a gene expression pattern, a genetic pathway, and a combination thereof; performing on the computer a cluster analysis to identify one or more of the genes having no prior association with infertility that cluster with one or more of the genes known to be associated with infertility the cluster analysis based on feature commonalities between the genes that have no prior association and the genes known to be associated with infertility; and identifying at least one of the genes having no prior association with infertility as a potential infertility biomarker based on it clustering with one or more genes known to be associated with infertility.
 11. The computer-implemented method of claim 10, wherein the cluster analysis comprises the steps of: identifying at least one feature of one or more of the genes known to be associated with infertility; analyzing one or more of the genes having no prior association with infertility for a corresponding feature; and comparing the at least one feature of the genes known to be associated with infertility with the corresponding feature of the genes having no prior association with infertility to identify feature commonalities.
 12. The computer-implemented method of claim 11, wherein the cluster analysis further comprises categorizing the one or more genes having no prior association with infertility into one or more feature groups based on the comparison.
 13. The computer-implemented method of claim 12, wherein the cluster analysis further comprises assigning a value to the categorized genes based on a degree of relatedness.
 14. The computer-implemented method of claim 10, wherein the one or more genes having no prior association with infertility are mammalian genes.
 15. The computer-implemented method of claim 14, wherein the mammalian genes correspond to a species selected from mouse, human, and a combination thereof.
 16. The computer-implemented method of claim 10, wherein the genes known to be associated with infertility are mammalian genes.
 17. The computer-implemented method of claim 16, wherein the mammalian genes correspond to a species selected from mouse, human, and a combination thereof.
 18. The computer-implemented method of claim 11, wherein the cluster analysis comprises: compiling the multi-modal data to form a matrix that will be used to exhibit the cluster analysis, wherein each domain is expressed as a row and each feature is expressed as a column; applying a distance metric to each pair of rows and each pair of columns in the matrix; clustering, using hierarchical clustering, the rows and columns of the matrix in order to determine feature commonalities between the genes known to be associated with infertility and the genes having no prior association with infertility. 